dwh material

DWH Material

Version 1.0

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DWH Training -9739096158

REVISION HISTORY

The following table reflects all changes to this document.

Date Author / Contributor Version Reason for Change

01-Nov-2004 1.0 Initial Document

14-Sep-2010 1.1 Updated Document

Table of Contents

1 Introduction 4

1.1 Purpose 4

2 ORACLE 4

2.1 DEFINATIONS 4

NORMALIZATION: 5

First Normal Form: 5

Second Normal Form: 5

Third Normal Form: 5

Boyce-Codd Normal Form: 6

Fourth Normal Form: 6

ORACLE SET OF STATEMENTS: 6

Data Definition Language :(DDL) 6

Data Manipulation Language (DML) 6

Data Querying Language (DQL) 7

Data Control Language (DCL) 7

Transactional Control Language (TCL) 7

Syntaxes: 7

ORACLE JOINS: 9

Equi Join/Inner Join: 10

Non-Equi Join 10

Self Join 10

Natural Join 11

Cross Join 11

Outer Join 11

Left Outer Join 11

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Right Outer Join 11

Full Outer Join 12

What’s the difference between View and Materialized View? 12

View: 12

Materialized View: 13

Inline view: 13

Indexes: 19

Why hints Require? 19

Explain Plan: 21

Store Procedure: 23

Packages: 24

Triggers: 24

Data files Overview: 26

2.2 IMPORTANT QUERIES 27

3 DWH CONCEPTS 29

What is BI? 29

4 ETL-INFORMATICA 53

4.1 Informatica Overview 53

4.2 Informatica Scenarios: 90

4.3 Development Guidelines 97

4.4 Performance Tips 101

4.5 Unit Test Cases (UTP): 103

5 UNIX 106

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Detailed Design DocumentAutomation of Candidate

Extract and Load Process

1 Introduction

1.1 Purpose

The purpose of this document is to provide the detailed information

about DWH Concepts and Informatica based on real-time training.

2 ORACLE

2.1 DEFINATIONS

Organizations can store data on various media and in different formats, such as a hard-copy document

in a filing cabinet or data stored in electronic spreadsheets or in databases.

A database is an organized collection of information.

To manage databases, you need database management systems (DBMS). A DBMS is a program that

stores, retrieves, and modifies data in the database on request. There are four main types of databases:

hierarchical, network, relational, and more recently object relational(ORDBMS).

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NORMALIZATION:

Some Oracle databases were modeled according to the rules of normalization that were intended to eliminate redundancy.

Obviously, the rules of normalization are required to understand your relationships and functional dependencies

First Normal Form:

A row is in first normal form (1NF) if all underlying domains contain atomic values only.

Eliminate duplicative columns from the same table. Create separate tables for each group of related data and identify each

row with a unique column or set of columns (the primary key).

Second Normal Form:

An entity is in Second Normal Form (2NF) when it meets the requirement of being in First Normal Form (1NF) and additionally:

Does not have a composite primary key. Meaning that the primary key can not be subdivided into separate logical entities.

All the non-key columns are functionally dependent on the entire primary key.

A row is in second normal form if, and only if, it is in first normal form and every non-key attribute is fully dependent on the key.

2NF eliminates functional dependencies on a partial key by putting the fields in a separate table from those that are dependent on the whole key. An example is resolving many: many relationships using an intersecting entity.

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Third Normal Form:

An entity is in Third Normal Form (3NF) when it meets the requirement of being in Second Normal Form (2NF) and additionally:

Functional dependencies on non-key fields are eliminated by putting them in a separate table. At this level, all non-key fields are dependent on the primary key.

A row is in third normal form if and only if it is in second normal form and if attributes that do not contribute to a description of the primary key are move into a separate table. An example is creating look-up tables.

Boyce-Codd Normal Form:

Boyce Codd Normal Form (BCNF) is a further refinement of 3NF. In his later writings Codd refers to BCNF as 3NF. A row is in Boyce Codd normal form if, and only if, every determinant is a candidate key. Most entities in 3NF are already in BCNF.

Fourth Normal Form:

An entity is in Fourth Normal Form (4NF) when it meets the requirement of being in Third Normal Form (3NF) and additionally:

Has no multiple sets of multi-valued dependencies. In other words, 4NF states that no entity can have more than a single one-to-many relationship.

ORACLE SET OF STATEMENTS:

Data Definition Language :(DDL)

Create

Alter

Drop

Truncate

Data Manipulation Language (DML)

Insert

Update

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Delete

Data Querying Language (DQL)

Select

Data Control Language (DCL)

Grant

Revoke

Transactional Control Language (TCL)

Commit

Rollback

Save point

Syntaxes:

CREATE OR REPLACE SYNONYM HZ_PARTIES FOR SCOTT.HZ_PARTIES

CREATE DATABASE LINK CAASEDW CONNECT TO ITO_ASA IDENTIFIED BY exact123 USING ' CAASEDW’

Materialized View syntax:

CREATE MATERIALIZED VIEW EBIBDRO.HWMD_MTH_ALL_METRICS_CURR_VIEW

REFRESH COMPLETE

START WITH sysdate

NEXT TRUNC(SYSDATE+1)+ 4/24

WITH PRIMARY KEY

AS

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select * from HWMD_MTH_ALL_METRICS_CURR_VW;

Another Method to refresh:

DBMS_MVIEW.REFRESH('MV_COMPLEX', 'C');

Case Statement:

Select NAME, (CASE WHEN (CLASS_CODE = 'Subscription') THEN ATTRIBUTE_CATEGORY ELSE TASK_TYPE END) TASK_TYPE, CURRENCY_CODE From EMP

Decode()

Select empname,Decode(address,’HYD’,’Hyderabad’, ‘Bang’, Bangalore’, address) as address from emp;

Procedure:

CREATE OR REPLACE PROCEDURE Update_bal (

cust_id_IN In NUMBER,

In amount_IN NUMBER DEFAULT 1) AS

BEGIN

Update account_tbl Set amount= amount_IN where cust_id= cust_id_IN

End

Trigger:

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CREATE OR REPLACE TRIGGER EMP_AUR

AFTER/BEFORE UPDATE ON EMP

REFERENCING

NEW AS NEW

OLD AS OLD

FOR EACH ROW

DECLARE

BEGIN

IF (:NEW.last_upd_tmst <> :OLD.last_upd_tmst) THEN

-- Insert into Control table record

Insert into table emp_w values('wrk',sysdate)

ELSE

-- Exec procedure

Exec update_sysdate()

END;

ORACLE JOINS:

Equi join

Non-equi join

Self join

Natural join of 112


Cross join

Outer join

Left outer

Right outer

Full outer

Equi Join/Inner Join:

SQL> select empno,ename,job,dname,loc from emp e,dept d where e.deptno=d.deptno;

USING CLAUSE

SQL> select empno,ename,job ,dname,loc from emp e join dept d

using(deptno);

ON CLAUSE

SQL> select empno,ename,job,dname,loc from emp e join dept d

on(e.deptno=d.deptno);

Non-Equi Join

A join which contains an operator other than ‘=’ in the joins condition.

Ex: SQL> select empno,ename,job,dname,loc from emp e,dept d where

e.deptno > d.deptno;

Self Join

Joining the table itself is called self join.

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Ex: SQL> select e1.empno,e2.ename,e1.job,e2.deptno from emp e1,emp e2

where e1.empno=e2.mgr;

Natural Join

Natural join compares all the common columns.

Ex: SQL> select empno,ename,job,dname,loc from emp natural join dept;

Cross Join

This will gives the cross product.

Ex: SQL> select empno,ename,job,dname,loc from emp cross join dept;

Outer Join

Outer join gives the non-matching records along with matching records.

Left Outer Join

This will display the all matching records and the records which are in left hand

side table those that are not in right hand side table.

Ex: SQL> select empno,ename,job,dname,loc from emp e left outer join dept

d on(e.deptno=d.deptno);

Or

SQL> select empno,ename,job,dname,loc from emp e,dept d where

e.deptno=d.deptno(+);

Right Outer Join

This will display the all matching records and the records which are in right

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Ex: SQL> select empno,ename,job,dname,loc from emp e right outer join

dept d on(e.deptno=d.deptno);

Or

SQL> select empno,ename,job,dname,loc from emp e,dept d where

e.deptno(+) = d.deptno;

Full Outer Join

This will display the all matching records and the non-matching records from

both tables.

Ex: SQL> select empno,ename,job,dname,loc from emp e full outer join dept

d on(e.deptno=d.deptno);

OR

SQL> select p.part_id, s.supplier_name 2 from part p, supplier s 3 where p.supplier_id = s.supplier_id (+) 4 union 5 select p.part_id, s.supplier_name 6 from part p, supplier s 7 where p.supplier_id (+) = s.supplier_id;

What’s the difference between View and Materialized View?

View:

Why Use Views?

• To restrict data access

• To make complex queries easy

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• To provide data independence

A simple view is one that:

– Derives data from only one table

– Contains no functions or groups of data

– Can perform DML operations through the view.

A complex view is one that:

– Derives data from many tables

– Contains functions or groups of data

– Does not always allow DML operations through the view

A view has a logical existence but a materialized view has a physical existence.Moreover a materialized view can be Indexed, analysed and so on....that is all the things that we can do with a table can also be done with a materialized view.

We can keep aggregated data into materialized view. we can schedule the MV to refresh but table can’t.MV can be created based on multiple tables.

Materialized View:

In DWH materialized views are very essential because in reporting side if we do aggregate calculations as per the business requirement report performance would be de graded. So to improve report performance rather than doing report calculations and joins at reporting side if we put same logic in the MV then we can directly select the data from MV without any joins and aggregations. We can also schedule MV (Materialize View).

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Inline view:

If we write a select statement in from clause that is nothing but inline view.

Ex:Get dept wise max sal along with empname and emp no.

Select a.empname, a.empno, b.sal, b.deptno From EMP a, (Select max (sal) sal, deptno from EMP group by deptno) bWherea.sal=b.sal anda.deptno=b.deptno

What is the difference between view and materialized view?

View Materialized view

A view has a logical existence. It does not contain data.

A materialized view has a physical existence.

Its not a database object. It is a database object.

We can perform DML operation on view.

We cannot perform DML operation on materialized view.

When we do select * from view it will fetch the data from base table.

When we do select * from materialized view it will fetch the data from materialized view.

In view we cannot schedule to refresh.

In materialized view we can schedule to refresh.

We can keep aggregated data into materialized view. Materialized view can be created based on multiple tables.

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DELETE

The DELETE command is used to remove rows from a table. A WHERE clause can be used to only remove some rows. If no WHERE condition is specified, all rows will be removed. After performing a DELETE operation you need to COMMIT or ROLLBACK the transaction to make the change permanent or to undo it.

TRUNCATE

TRUNCATE removes all rows from a table. The operation cannot be rolled back. As such, TRUCATE is faster and doesn't use as much undo space as a DELETE.

DROP

The DROP command removes a table from the database. All the tables' rows, indexes and privileges will also be removed. The operation cannot be rolled back.

Difference between Rowid and Rownum?

ROWID

A globally unique identifier for a row in a database. It is created at the time the row is inserted into a table, and destroyed when it is removed from a table.'BBBBBBBB.RRRR.FFFF' where BBBBBBBB is the block number, RRRR is the slot(row) number, and FFFF is a file number.

ROWNUM

For each row returned by a query, the ROWNUM pseudo column returns a number indicating the order in which Oracle selects the row from a table or set of joined rows. The first row selected has a ROWNUM of 1, the second has 2, and so on.

You can use ROWNUM to limit the number of rows returned by a query, as in this example:

SELECT * FROM employees WHERE ROWNUM < 10;

Rowid Row-num

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allocated every time a new record is inserted in a table. This ID is unique and cannot be changed by the user.

by a select statement.

Rowid is permanent. Row-num is temporary.

Rowid is a globally unique identifier for a row in a database. It is created at the time the row is inserted into the table, and destroyed when it is removed from a table.

The row-num pseudocoloumn returns a number indicating the order in which oracle selects the row from a table or set of joined rows.

Order of where and having:

SELECT column, group_function

FROM table

[WHERE condition]

[GROUP BY group_by_expression]

[HAVING group_condition]

[ORDER BY column];

The WHERE clause cannot be used to restrict groups. you use the

HAVING clause to restrict groups.

Differences between where clause and having clause

Where clause Having clause

Both where and having clause can be used to filter the data.

Where as in where clause it is not But having clause we need to use it

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mandatory. with the group by.

Where clause applies to the individual rows.

Where as having clause is used to test some condition on the group rather than on individual rows.

Where clause is used to restrict rows. But having clause is used to restrict groups.

Restrict normal query by where Restrict group by function by having

In where clause every record is filtered based on where.

In having clause it is with aggregate records (group by functions).

MERGE Statement

You can use merge command to perform insert and update in a single command.

Ex: Merge into student1 s1

Using (select * from student2) s2

On (s1.no=s2.no)

When matched then

Update set marks = s2.marks

When not matched then

Insert (s1.no, s1.name, s1.marks) Values (s2.no, s2.name, s2.marks);

What is the difference between sub-query & co-related sub query?

A sub query is executed once for the parent statement

whereas the correlated sub query is executed once for each

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row of the parent query.

Sub Query:

Example:

Select deptno, ename, sal from emp a where sal in (select sal from Grade where sal_grade=’A’ or sal_grade=’B’)

Co-Related Sun query:

Example:

Find all employees who earn more than the average salary in their department.

SELECT last-named, salary, department_id FROM employees A

WHERE salary > (SELECT AVG (salary)

FROM employees B WHERE B.department_id =A.department_id

Group by B.department_id)

EXISTS:

The EXISTS operator tests for existence of rows in

the results set of the subquery.

Select dname from dept where exists (select 1 from EMPwhere dept.deptno= emp.deptno);

Sub-query Co-related sub-query

A sub-query is executed once for the parent Query

Where as co-related sub-query is executed once for each row of the parent query.

Example:

Select * from emp where deptno in (select deptno from dept);

Example:

Select a.* from emp e where sal >= (select avg(sal) from emp a where

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a.deptno=e.deptno group by a.deptno);

Indexes:

1. Bitmap indexes are most appropriate for columns having low distinct values—such as GENDER, MARITAL_STATUS, and RELATION. This assumption is not completely accurate, however. In reality, a bitmap index is always advisable for systems in which data is not frequently updated by many concurrent systems. In fact, as I'll demonstrate here, a bitmap index on a column with 100-percent unique values (a column candidate for primary key) is as efficient as a B-tree index.

2. When to Create an Index

3. You should create an index if:

4. A column contains a wide range of values

5. A column contains a large number of null values

6. One or more columns are frequently used together in a WHERE clause or a join condition

7. The table is large and most queries are expected to retrieve less than 2 to 4 percent of the rows

8. By default if u create index that is nothing but b-tree index.

Why hints Require?

It is a perfect valid question to ask why hints should be used. Oracle comes with an optimizer that promises to optimize a query's execution plan. When this optimizer is really doing a good job, no hints should be required at all.

Sometimes, however, the characteristics of the data in the database are changing rapidly, so that the optimizer (or more accuratly, its statistics) are out of date. In this case, a hint could help.

You should first get the explain plan of your SQL and determine what changes

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http://www.adp-gmbh.ch/ora/sql/execution_plan.html

http://www.adp-gmbh.ch/ora/tuning/optimizer.html

can be done to make the code operate without using hints if possible. However, hints such as ORDERED, LEADING, INDEX, FULL, and the various AJ and SJ hints can take a wild optimizer and give you optimal performance

Tables analyze and update Analyze Statement

The ANALYZE statement can be used to gather statistics for a specific table, index or cluster. The statistics can be computed exactly, or estimated based on a specific number of rows, or a percentage of rows:

ANALYZE TABLE employees COMPUTE STATISTICS;

ANALYZE TABLE employees ESTIMATE STATISTICS SAMPLE 15 PERCENT;

EXEC DBMS_STATS.gather_table_stats('SCOTT', 'EMPLOYEES');

Automatic Optimizer Statistics Collection

By default Oracle 10g automatically gathers optimizer statistics using a scheduled job called GATHER_STATS_JOB. By default this job runs within maintenance windows between 10 P.M. to 6 A.M. week nights and all day on weekends. The job calls the DBMS_STATS.GATHER_DATABASE_STATS_JOB_PROC internal procedure which gathers statistics for tables with either empty or stale statistics, similar to the DBMS_STATS.GATHER_DATABASE_STATS procedure using the GATHER AUTO option. The main difference is that the internal job prioritizes the work such that tables most urgently requiring statistics updates are processed first.

Hint categories:

Hints can be categorized as follows:

ALL_ROWSOne of the hints that 'invokes' the Cost based optimizer ALL_ROWS is usually used for batch processing or data warehousing systems.

(/*+ ALL_ROWS */)

FIRST_ROWSOne of the hints that 'invokes' the Cost based optimizer

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FIRST_ROWS is usually used for OLTP systems.

(/*+ FIRST_ROWS */)

CHOOSEOne of the hints that 'invokes' the Cost based optimizer This hint lets the server choose (between ALL_ROWS and FIRST_ROWS, based on statistics gathered.

Hints for Join Orders,

Hints for Join Operations,

Hints for Parallel Execution, (/*+ parallel(a,4) */) specify degree either 2 or 4 or 16

Additional Hints

HASHHashes one table (full scan) and creates a hash index for that table. Then hashes other table and uses hash index to find corresponding records. Therefore not suitable for < or > join conditions.

/*+ use_hash */

Use Hint to force using index

SELECT /*+INDEX (TABLE_NAME INDEX_NAME) */ COL1,COL2 FROM TABLE_NAME

Select ( /*+ hash */ ) empno from

ORDERED- This hint forces tables to be joined in the order specified. If you know table X has fewer rows, then ordering it first may speed execution in a join.

PARALLEL (table, instances)This specifies the operation is to be done in parallel.

If index is not able to create then will go for /*+ parallel(table, 8)*/-----For select and update example---in where clase like st,not in ,>,< ,<> then we will use.

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Explain Plan:

Explain plan will tell us whether the query properly using indexes or not.whatis the cost of the table whether it is doing full table scan or not, based on these statistics we can tune the query.The explain plan process stores data in the PLAN_TABLE. This table can be located in the current schema or a shared schema and is created using in SQL*Plus as follows:

SQL> CONN sys/password AS SYSDBAConnectedSQL> @$ORACLE_HOME/rdbms/admin/utlxplan.sqlSQL> GRANT ALL ON sys.plan_table TO public;

SQL> CREATE PUBLIC SYNONYM plan_table FOR sys.plan_table;

What is your tuning approach if SQL query taking long time? Or how do u tune SQL query?

If query taking long time then First will run the query in Explain Plan, The explain plan process stores data in the PLAN_TABLE.

it will give us execution plan of the query like whether the query is using the relevant indexes on the joining columns or indexes to support the query are missing.

If joining columns doesn’t have index then it will do the full table scan if it is full table scan the cost will be more then will create the indexes on the joining columns and will run the query it should give better performance and also needs to analyze the tables if analyzation happened long back. The ANALYZE statement can be used to gather statistics for a specific table, index or cluster using

ANALYZE TABLE employees COMPUTE STATISTICS;

If still have performance issue then will use HINTS, hint is nothing but a clue. We can use hints like

ALL_ROWSOne of the hints that 'invokes' the Cost based optimizer ALL_ROWS is usually used for batch processing or data warehousing

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systems.

(/*+ ALL_ROWS */)

FIRST_ROWSOne of the hints that 'invokes' the Cost based optimizer FIRST_ROWS is usually used for OLTP systems.

(/*+ FIRST_ROWS */)

CHOOSEOne of the hints that 'invokes' the Cost based optimizer This hint lets the server choose (between ALL_ROWS and FIRST_ROWS, based on statistics gathered.

HASHHashes one table (full scan) and creates a hash index for that table. Then hashes other table and uses hash index to find corresponding records. Therefore not suitable for < or > join conditions.

/*+ use_hash */

Hints are most useful to optimize the query performance.

Store Procedure:

What are the differences between stored procedures and triggers?

Stored procedure normally used for performing tasksBut the Trigger normally used for tracing and auditing logs.

Stored procedures should be called explicitly by the user in order to executeBut the Trigger should be called implicitly based on the events defined in the table.

Stored Procedure can run independentlyBut the Trigger should be part of any DML events on the table.

Stored procedure can be executed from the TriggerBut the Trigger cannot be executed from the Stored procedures.

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Stored Procedures can have parameters.But the Trigger cannot have any parameters.

Stored procedures are compiled collection of programs or SQL statements in the database.

Using stored procedure we can access and modify data present in many tables.

Also a stored procedure is not associated with any particular database object.

But triggers are event-driven special procedures which are attached to a specific database object say a table.

Stored procedures are not automatically run and they have to be called explicitly by the user. But triggers get executed when the particular event associated with the event gets fired.

Packages:

Packages provide a method of encapsulating related procedures, functions, and associated cursors and variables together as a unit in the database.

package that contains several procedures and functions that process related to same transactions.

A package is a group of related procedures and functions, together with the cursors and variables they use,

Packages provide a method of encapsulating related procedures, functions, and associated cursors and variables together as a unit in the database.

Triggers:

Oracle lets you define procedures called triggers that run implicitly when an INSERT, UPDATE, or DELETE statement is issued against the associated table

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Triggers are similar to stored procedures. A trigger stored in the database can include SQL and PL/SQL

Types of Triggers

This section describes the different types of triggers:

Row Triggers and Statement Triggers

BEFORE and AFTER Triggers

INSTEAD OF Triggers

Triggers on System Events and User Events

Row Triggers

A row trigger is fired each time the table is affected by the triggering statement. For example, if an UPDATE statement updates multiple rows of a table, a row trigger is fired once for each row affected by the UPDATE statement. If a triggering statement affects no rows, a row trigger is not run.

BEFORE and AFTER Triggers

When defining a trigger, you can specify the trigger timing--whether the trigger action is to be run before or after the triggering statement. BEFORE and AFTER apply to both statement and row triggers.

BEFORE and AFTER triggers fired by DML statements can be defined only on tables, not on views.

Difference between Trigger and Procedure

Triggers Stored Procedures

In trigger no need to execute manually. Triggers will be fired automatically.

Triggers that run implicitly when an INSERT, UPDATE, or DELETE statement is issued against the

Where as in procedure we need to execute manually.

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associated table.

Differences between stored procedure and functions

Stored Procedure Functions

Stored procedure may or may not return values.

Function should return at least one output parameter. Can return more than one parameter using OUT argument.

Stored procedure can be used to solve the business logic.

Function can be used to calculations

Stored procedure is a pre-compiled statement.

But function is not a pre-compiled statement.

Stored procedure accepts more than one argument.

Whereas function does not accept arguments.

Stored procedures are mainly used to process the tasks.

Functions are mainly used to compute values

Cannot be invoked from SQL statements. E.g. SELECT

Can be invoked form SQL statements e.g. SELECT

Can affect the state of database using commit.

Cannot affect the state of database.

Stored as a pseudo-code in database i.e. compiled form.

Parsed and compiled at runtime.

Data files Overview:

A tablespace in an Oracle database consists of one or more physical datafiles. A datafile can be associated with only one tablespace and only one database.

Table Space:

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Oracle stores data logically in tablespaces and physically in datafiles associated with the corresponding tablespace.

A database is divided into one or more logical storage units called tablespaces. Tablespaces are divided into logical units of storage called segments.

Control File:

A control file contains information about the associated database that is required for access by an instance, both at startup and during normal operation. Control file information can be modified only by Oracle; no database administrator or user can edit a control file.

2.2 IMPORTANT QUERIES

1. Get duplicate rows from the table:

Select empno, count (*) from EMP group by empno having count (*)>1;

2. Remove duplicates in the table:

Delete from EMP where rowid not in (select max (rowid) from EMP group by empno);

3. Below query transpose columns into rows.

Name No Add1 Add2

abc 100 hyd bang

xyz 200 Mysore pune

Select name, no, add1 from A

UNION

Select name, no, add2 from A;

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4. Below query transpose rows into columns.

select

emp_id,

max(decode(row_id,0,address))as address1,

max(decode(row_id,1,address)) as address2,

max(decode(row_id,2,address)) as address3

from (select emp_id,address,mod(rownum,3) row_id from temp order by emp_id )

group by emp_id

Other query:

select

emp_id,

max(decode(rank_id,1,address)) as add1,

max(decode(rank_id,2,address)) as add2,

max(decode(rank_id,3,address))as add3

from

(select emp_id,address,rank() over (partition by emp_id order by emp_id,address )rank_id from temp )

group by

emp_id

5. Rank query:

Select empno, ename, sal, r from (select empno, ename, sal, rank () over (order by sal desc) r from EMP);

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The DENSE_RANK function works acts like the RANK function except that it assigns consecutive ranks:

Select empno, ename, Sal, from (select empno, ename, sal, dense_rank () over (order by sal desc) r from emp);

7. Top 5 salaries by using rank:

Select empno, ename, sal,r from (select empno,ename,sal,dense_rank() over (order by sal desc) r from emp) where r<=5;

Or

Select * from (select * from EMP order by sal desc) where rownum<=5;

8. 2 nd highest Sal:

Select empno, ename, sal, r from (select empno, ename, sal, dense_rank () over (order by sal desc) r from EMP) where r=2;

9. Top sal:

Select * from EMP where sal= (select max (sal) from EMP);

10.How to display alternative rows in a table?

SQL> select *from emp where (rowid, 0) in (select rowid,mod(rownum,2) from emp);

11.Hierarchical queries

Starting at the root, walk from the top down, and eliminate employee Higgins in the result, but

process the child rows.

SELECT department_id, employee_id, last_name, job_id, salary

FROM employees

WHERE last_name! = ’Higgins’

START WITH manager_id IS NULL

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CONNECT BY PRIOR employee_id = menagerie;

3 DWH CONCEPTS

What is BI?

Business Intelligence refers to a set of methods and techniques that are used by organizations for tactical and strategic decision making. It leverages methods and technologies that focus on counts, statistics and business objectives to improve business performance.

The objective of Business Intelligence is to better understand customers and improve customer service, make the supply and distribution chain more efficient, and to identify and address business problems and opportunities quickly.

Warehouse is used for high level data analysis purpose.It is used for predictions, timeseries analysis, financial Analysis, what -if simulations etc. Basically it is used for better decision making.

What is a Data Warehouse?

Data Warehouse is a "Subject-Oriented, Integrated, Time-Variant Nonvolatile collection of data in support of decision making".

In terms of design data warehouse and data mart are almost the same.

In general a Data Warehouse is used on an enterprise level and a Data Marts is used on a business division/department level.

Subject Oriented:

Data that gives information about a particular subject instead of about a company's ongoing operations.

Integrated:

Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.

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Time-variant:

All data in the data warehouse is identified with a particular time period.

Non-volatile:

Data is stable in a data warehouse. More data is added but data is never removed.

What is a DataMart?

Datamart is usually sponsored at the department level and developed with a specific details or subject in mind, a Data Mart is a subset of data warehouse with a focused objective.

What is the difference between a data warehouse and a data mart?

In terms of design data warehouse and data mart are almost the same.

In general a Data Warehouse is used on an enterprise level and a Data Marts is used on a business division/department level.

A data mart only contains data specific to a particular subject areas.

Difference between data mart and data warehouse

Data Mart Data Warehouse

Data mart is usually sponsored at the department level and developed with a specific issue or subject in mind, a data mart is a data warehouse with a focused objective.

Data warehouse is a “Subject-Oriented, Integrated, Time-Variant, Nonvolatile collection of data in support of decision making”.

A data mart is used on a business division/ department level.

A data warehouse is used on an enterprise level

A Data Mart is a subset of data from a Data Warehouse. Data Marts are built for specific user groups.

A Data Warehouse is simply an integrated consolidation of data from a variety of sources that is specially designed to support strategic and

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tactical decision making.

By providing decision makers with only a subset of data from the Data Warehouse, Privacy, Performance and Clarity Objectives can be attained.

The main objective of Data Warehouse is to provide an integrated environment and coherent picture of the business at a point in time.

what is fact less fact table?

A fact table that contains only primary keys from the dimension tables, and that do not contain any measures that type of fact table is called fact less fact table .

What is a Schema?

Graphical Representation of the datastructure.

First Phase in implementation of Universe

What are the most important features of a data warehouse?

DRILL DOWN, DRILL ACROSS, Graphs, PI charts, dashboards and TIME HANDLING

To be able to drill down/drill across is the most basic requirement of an end user in a data warehouse. Drilling down most directly addresses the natural end-user need to see more detail in an result. Drill down should be as generic as possible becuase there is absolutely no good way to predict users drill-down path.

What does it mean by grain of the star schema?

In Data warehousing grain refers to the level of detail available in a given fact table as well as to the level of detail provided by a star schema.

It is usually given as the number of records per key within the table. In general, the grain of the fact table is the grain of the star schema.

What is a star schema?

Star schema is a data warehouse schema where there is only one "fact table" of 112


and many denormalized dimension tables.

Fact table contains primary keys from all the dimension tables and other numeric columns columns of additive, numeric facts.

What is a snowflake schema?

Unlike Star-Schema, Snowflake schema contain normalized dimension tables in a tree like structure with many nesting levels.

Snowflake schema is easier to maintain but queries require more joins.

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What is the difference between snow flake and star schema

Star Schema Snow Flake Schema

The star schema is the simplest data warehouse scheme.

Snowflake schema is a more complex data warehouse model than a star schema.

In star schema each of the dimensions is represented in a single table .It should not have any hierarchies between dims.

In snow flake schema at least one hierarchy should exists between dimension tables.

It contains a fact table surrounded by dimension tables. If the dimensions are de-normalized, we say it is a star schema design.

It contains a fact table surrounded by dimension tables. If a dimension is normalized, we say it is a snow flaked design.

In star schema only one join establishes the relationship between the fact table and any one of the dimension tables.

In snow flake schema since there is relationship between the dimensions tables it has to do many joins to fetch the data.

A star schema optimizes the performance by keeping queries simple and providing fast response time. All the information about the each level is stored in one row.

Snowflake schemas normalize dimensions to eliminated redundancy. The result is more complex queries and reduced query performance.

It is called a star schema because the diagram resembles a star.

It is called a snowflake schema because the diagram resembles a snowflake.

What is Fact and Dimension?

A "fact" is a numeric value that a business wishes to count or sum. A "dimension" is essentially an entry point for getting at the facts. Dimensions are things of interest to the business.

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A set of level properties that describe a specific aspect of a business, used for analyzing the factual measures.

What is Fact Table?

A Fact Table in a dimensional model consists of one or more numeric facts of importance to a business. Examples of facts are as follows:

the number of products sold

the value of products sold

the number of products produced

the number of service calls received

What is Factless Fact Table?

Factless fact table captures the many-to-many relationships between dimensions, but contains no numeric or textual facts. They are often used to record events or coverage information.

Common examples of factless fact tables include:

Identifying product promotion events (to determine promoted products that didn’t sell)

Tracking student attendance or registration events

Tracking insurance-related accident events

Types of facts?

There are three types of facts:

Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table.

Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.

Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.

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What is Granularity?

Principle: create fact tables with the most granular data possible to support analysis of the business process.

In Data warehousing grain refers to the level of detail available in a given fact table as well as to the level of detail provided by a star schema.

It is usually given as the number of records per key within the table. In general, the grain of the fact table is the grain of the star schema.

Facts: Facts must be consistent with the grain.all facts are at a uniform grain.

Watch for facts of mixed granularity

Total sales for day & montly total

Dimensions: each dimension associated with fact table must take on a single value for each fact row.

Each dimension attribute must take on one value.

Outriggers are the exception, not the rule.

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Dimensional Model

What is slowly Changing Dimension?

Slowly changing dimensions refers to the change in dimensional attributes over time.

An example of slowly changing dimension is a Resource dimension where attributes of a particular employee change over time like their designation

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changes or dept changes etc.

What is Conformed Dimension?

Conformed Dimensions (CD): these dimensions are something that is built once in your model and can be reused multiple times with different fact tables. For example, consider a model containing multiple fact tables, representing different data marts. Now look for a dimension that is common to these facts tables. In this example let’s consider that the product dimension is common and hence can be reused by creating short cuts and joining the different fact tables.Some of the examples are time dimension, customer dimensions, product dimension.

What is Junk Dimension?

A "junk" dimension is a collection of random transactional codes, flags and/or text attributes that are unrelated to any particular dimension. The junk dimension is simply a structure that provides a convenient place to store the junk attributes. A good example would be a trade fact in a company that brokers equity trades.

When you consolidate lots of small dimensions and instead of having 100s of small dimensions, that will have few records in them, cluttering your database with these mini ‘identifier’ tables, all records from all these small dimension tables are loaded into ONE dimension table and we call this dimension table Junk dimension table. (Since we are storing all the junk in this one table) For example: a company might have handful of manufacture plants, handful of order types, and so on, so forth, and we can consolidate them in one dimension table called junked dimension table

It’s a dimension table which is used to keep junk attributes

What is De Generated Dimension?

An item that is in the fact table but is stripped off of its description, because the description belongs in dimension table, is referred to as Degenerated Dimension. Since it looks like dimension, but is really in fact table and has been degenerated of its description, hence is called degenerated dimension..

Degenerated Dimension: a dimension which is located in fact table known as Degenerated dimension

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Dimensional Model:

A type of data modeling suited for data warehousing. In a dimensional model, there are two types of tables: dimensional tables and fact tables. Dimensional table records information on each dimension, and fact table records all the "fact", or measures.

Data modeling

There are three levels of data modeling. They are conceptual, logical, and physical. This section will explain the difference among the three, the order with which each one is created, and how to go from one level to the other.

Conceptual Data Model

Features of conceptual data model include:

Includes the important entities and the relationships among them.

No attribute is specified.

No primary key is specified.

At this level, the data modeler attempts to identify the highest-level relationships among the different entities.

Logical Data Model

Features of logical data model include:

Includes all entities and relationships among them.

All attributes for each entity are specified.

The primary key for each entity specified.

Foreign keys (keys identifying the relationship between different entities) are specified.

Normalization occurs at this level.

At this level, the data modeler attempts to describe the data in as much detail as possible, without regard to how they will be physically implemented in the database.

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http://www.1keydata.com/datawarehousing/data-modeling-levels.html

In data warehousing, it is common for the conceptual data model and the logical data model to be combined into a single step (deliverable).

The steps for designing the logical data model are as follows:

1. Identify all entities.

2. Specify primary keys for all entities.

3. Find the relationships between different entities.

4. Find all attributes for each entity.

5. Resolve many-to-many relationships.

6. Normalization.

Physical Data Model

Features of physical data model include:

Specification all tables and columns.

Foreign keys are used to identify relationships between tables.

Demoralization may occur based on user requirements.

Physical considerations may cause the physical data model to be quite different from the logical data model.

At this level, the data modeler will specify how the logical data model will be realized in the database schema.

The steps for physical data model design are as follows:

1. Convert entities into tables.

2. Convert relationships into foreign keys.

3. Convert attributes into columns.

9. http://www.learndatamodeling.com/dm_standard.htm

10.Modeling is an efficient and effective way to represent the organization’s needs; It provides information in a graphical way to the

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members of an organization to understand and communicate the business rules and processes. Business Modeling and Data Modeling are the two important types of modeling.

The differences between a logical data model and physical data model is shown below.

Logical vs Physical Data Modeling

Logical Data Model Physical Data Model

Represents business information and defines business rules

Represents the physical implementation of the model in a database.

Entity Table

Attribute Column

Primary Key Primary Key Constraint

Alternate Key Unique Constraint or Unique Index

Inversion Key Entry Non Unique Index

Rule Check Constraint, Default Value

Relationship Foreign Key

Definition Comment

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Below is the simple data model

Below is the sq for project dim

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EDIII – Logical Design

ACW_PCBA_APPROVAL_FPrimary Key

PCBA_APPROVAL_KEY[PK1]

Non-Key AttributesPART_KEYCISCO_PART_NUMBERSUPPLY_CHANNEL_KEYNPIAPPROVAL_FLAGADJUSTMENTAPPROVAL_DATEADJUSTMENT_AMTSPEND_BY_ASSEMBLYCOMM_MGR_KEYBUYER_IDRFQ_CREATEDRFQ_RESPONSECSSD_CREATED_BYD_CREATED_DATED_LAST_UPDATED_BYD_LAST_UPDATE_DATE

ACW_DF_APPROVAL_FPrimary Key

DF_APPROVAL_KEY[PK1]

Non-Key AttributesPART_KEYCISCO_PART_NUMBERSUPPLY_CHANNEL_KEYPCBA_ITEM_FLAGAPPROVEDAPPROVAL_DATEBUYER_IDRFQ_CREATEDRFQ_RESPONSECSSD_CREATED_BYD_CREATION_DATED_LAST_UPDATED_BYD_LAST_UPDATE_DATE

ACW_USERS_DPrimary Key

USER_KEY [PK1]Non-Key Attributes

PERSON_IDEMAIL_ADDRESSLAST_NAMEFIRST_NAMEFULL_NAMEEFFECTIVE_START_DATEEFFECTIVE_END_DATEEMPLOYEE_NUMBERLAST_UPDATED_BYLAST_UPDATE_DATECREATION_DATECREATED_BYD_LAST_UPDATED_BYD_LAST_UPDATE_DATED_CREATION_DATED_CREATED_BY

ACW_DF_FEES_FPrimary Key

ACW_DF_FEES_KEY[PK1]

Non-Key AttributesPRODUCT_KEYORG_KEYDF_MGR_KEYCOST_REQUIREDDF_FEESCOSTED_BYCOSTED_DATEAPPROVING_MGRAPPROVED_DATED_CREATED_BYD_CREATION_DATED_LAST_UPDATE_BYD_LAST_UPDATED_DATE

ACW_PCBA_APPROVAL_STGNon-Key Attributes

INVENTORY_ITEM_IDLATEST_REVLOCATION_IDLOCATION_CODEAPPROVAL_FLAGADJUSTMENTAPPROVAL_DATETOTAL_ADJUSTMENTTOTAL_ITEM_COSTDEMANDCOMM_MGRBUYER_IDBUYERRFQ_CREATEDRFQ_RESPONSECSS

ACW_SUPPLY_CHANNEL_DPrimary Key

SUPPLY_CHANNEL_KEY[PK1]

Non-Key AttributesSUPPLY_CHANNELDESCRIPTIONLAST_UPDATED_BYLAST_UPDATE_DATECREATED_BYCREATION_DATED_LAST_UPDATED_BYD_LAST_UPDATE_DATED_CREATED_BYD_CREATION_DATE

ACW_PRODUCTS_DPrimary Key

PRODUCT_KEY [PK1]Non-Key Attributes

PRODUCT_NAMEBUSINESS_UNIT_IDBUSINESS_UNITPRODUCT_FAMILY_IDPRODUCT_FAMILYITEM_TYPED_CREATED_BYD_CREATION_DATED_LAST_UPDATE_BYD_LAST_UPDATED_DATE

ACW_DF_FEES_STGNon-Key Attributes

SEGMENT1ORGANIZATION_IDITEM_TYPEBUYER_IDCOST_REQUIREDQUARTER_1_COSTQUARTER_2_COSTQUARTER_3_COSTQUARTER_4_COSTCOSTED_BYCOSTED_DATEAPPROVED_BYAPPROVED_DATE

ACW_DF_APPROVAL_STGNon-Key Attributes

INVENTORY_ITEM_IDCISCO_PART_NUMBERLATEST_REVPCBA_ITEM_FLAGAPPROVAL_FLAGAPPROVAL_DATELOCATION_IDLOCATION_CODEBUYERBUYER_IDRFQ_CREATEDRFQ_RESPONSECSS

ACW_PART_TO_PID_DPrimary Key

PART_TO_PID_KEY [PK1]Non-Key Attributes

PART_KEYCISCO_PART_NUMBERPRODUCT_KEYPRODUCT_NAMELATEST_REVISIOND_CREATED_BYD_CREATION_DATED_LAST_UPDATED_BYD_LAST_UPDATE_DATE

ACW_ORGANIZATION_DPrimary Key

ORG_KEY [PK1]Non-Key Attributes

ORGANIZATION_CODECREATED_BYCREATION_DATELAST_UPDATE_DATELAST_UPDATED_BYD_CREATED_BYD_CREATION_DATED_LAST_UPDATE_DATED_LAST_UPDATED_BY

EDW_TIME_HIERARCHY

PID for DF Fees

Users

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EDII– Physical Design

ACW_PCBA_APPROVAL_FColumns

PCBA_APPROVAL_KEY CHAR(10) [PK1]PART_KEY NUMBER(10)CISCO_PART_NUMBER CHAR(10)SUPPLY_CHANNEL_KEYNUMBER(10)NPI CHAR(1)APPROVAL_FLAG CHAR(1)ADJUSTMENT CHAR(1)APPROVAL_DATE DAT EADJUSTMENT_AMT FLOAT(12)SPEND_BY_ASSEMBLYFLOAT(12)COMM_MGR_KEY NUMBER(10)BUYER_ID NUMBER(10)RFQ_CREATED CHAR(1)RFQ_RESPONSE CHAR(1)CSS CHAR(10)D_CREATED_BY CHAR(10)D_CREATED_DAT E CHAR(10)D_LAST_UPDATED_BY CHAR(10)D_LAST_UPDATE_DATEDAT E

ACW_DF_FEES_FColumns

ACW_DF_FEES_KEY NUMBER(10) [PK1]PRODUCT_KEY NUMBER(10)ORG_KEY NUMBER(10)DF_MGR_KEY NUMBER(10)COST_REQUIRED CHAR(1)DF_FEES FLOAT(12)COSTED_BY NUMBER(10)COSTED_DATE DAT EAPPROVING_MGR NUMBER(10)APPROVED_DATE DAT ED_CREATED_BY CHAR(10)D_CREATION_DATE DAT ED_LAST_UPDATE_BY CHAR(10)D_LAST_UPDATED_DAT ECHAR(10)

ACW_USERS_DColumns

USER_KEY NUMBER(10) [PK1]PERSON_ID CHAR(10)EMAIL_ADDRESS CHAR(10)LAST_NAM E VARCHAR2(50)FIRST _NAME VARCHAR2(50)FULL_NAM E CHAR(10)EFFECTIVE_START _DATEDAT EEFFECTIVE_END_DAT E DAT EEMPLOYEE_NUMBER NUMBER(10)SEX NUMBERLAST_UPDATE_DATE DAT ECREATION_DATE DAT ECREATED_BY NUMBER(10)D_LAST_UPDATED_BY CHAR(10)D_LAST_UPDATE_DATE DAT ED_CREATION_DATE DAT ED_CREATED_BY CHAR(10)

ACW_DF_APPROVAL_FColumns

DF_APPROVAL_KEY NUMBER(10) [PK1]PART_KEY NUMBER(10)CISCO_PART_NUMBER CHAR(30)SUPPLY_CHANNEL_KEYNUMBER(10)PCBA_ITEM_FLAG CHAR(1)APPROVED CHAR(1)APPROVAL_DATE DAT EBUYER_ID NUMBER(10)RFQ_CREATED CHAR(1)RFQ_RESPONSE CHAR(1)CSS CHAR(10)D_CREATED_BY CHAR(10)D_CREATION_DATE DAT ED_LAST_UPDATED_BY CHAR(10)D_LAST_UPDATE_DATEDAT E

ACW_PCBA_APPROVAL_STGColumns

INVENTORY_IT EM_IDNUMBER(10)LATEST _REV CHAR(10)LOCATION_ID NUMBER(10)LOCATION_CODE CHAR(10)APPROVAL_FLAG CHAR(1)ADJUSTMENT CHAR(1)APPROVAL_DATE DAT ETOTAL_ADJUSTMENTCHAR(10)TOTAL_ITEM _COST FLOAT(10)DEMAND NUMBERCOMM_MGR CHAR(10)BUYER_ID NUMBER(10)BUYER VARCHAR2(240)RFQ_CREATED CHAR(1)RFQ_RESPONSE CHAR(1)CSS CHAR(10)

ACW_PRODUCTS_DColumns

PRODUCT_KEY NUMBER(10) [PK1]PRODUCT_NAME CHAR(30)BUSINESS_UNIT_ID NUMBER(10)BUSINESS_UNIT VARCHAR2(60)PRODUCT_FAM ILY_ID NUMBER(10)PRODUCT_FAM ILY VARCHAR2(180)IT EM_TYPE CHAR(30)D_CREATED_BY CHAR(10)D_CREATION_DATE DAT ED_LAST_UPDATE_BY CHAR(10)D_LAST_UPDATED_DAT ECHAR(10)

ACW_SUPPLY_CHANNEL_DColumns

SUPPLY_CHANNEL_KEYNUMBER(10) [PK1]SUPPLY_CHANNEL CHAR(60)DESCRIPT ION VARCHAR2(240)LAST_UPDATED_BY NUMBERLAST_UPDATE_DATE DAT ECREATED_BY NUMBER(10)CREATION_DATE DAT ED_LAST_UPDATED_BY CHAR(10)D_LAST_UPDATE_DATEDAT ED_CREATED_BY CHAR(10)D_CREATION_DATE DAT E

ACW_DF_FEES_STGColumns

SEGMENT 1 VARCHAR2(40)ORGANIZATION_IDNUMBER(10)IT EM_TYPE CHAR(30)BUYER_ID NUMBER(10)COST_REQUIRED CHAR(1)QUARTER_1_COSTFLOAT(12)QUARTER_2_COSTFLOAT(12)QUARTER_3_COSTFLOAT(12)QUARTER_4_COSTFLOAT(12)COSTED_BY NUMBER(10)COSTED_DATE DAT EAPPROVED_BY NUMBER(10)APPROVED_DATE DAT E

ACW_DF_APPROVAL_STGColumns

INVENTORY_IT EM_ID NUMBER(10)CISCO_PART_NUMBERCHAR(30)LATEST _REV CHAR(10)PCBA_ITEM_FLAG CHAR(1)APPROVAL_FLAG CHAR(1)APPROVAL_DATE DAT ELOCATION_ID NUMBER(10)SUPPLY_CHANNEL CHAR(10)BUYER VARCHAR2(240)BUYER_ID NUMBER(10)RFQ_CREATED CHAR(1)RFQ_RESPONSE CHAR(1)CSS CHAR(10)

ACW_PART_TO_PID_DColumns

PART_T O_PID_KEY NUMBER(10) [PK1]PART_KEY NUMBER(10)CISCO_PART_NUMBERCHAR(30)PRODUCT_KEY NUMBER(10)PRODUCT_NAME CHAR(30)LATEST _REVIS ION CHAR(10)D_CREATED_BY CHAR(10)D_CREATION_DATE DAT ED_LAST_UPDATED_BYCHAR(10)D_LAST_UPDATE_DATEDAT E

ACW_ORGANIZAT ION_DColumns

ORG_KEY NUMBER(10) [PK1]ORGANIZATION_CODE CHAR(30)CREATED_BY NUMBER(10)CREATION_DATE DAT ELAST_UPDATE_DATE DAT ELAST_UPDATED_BY NUMBERD_CREATED_BY CHAR(10)D_CREATION_DATE DAT ED_LAST_UPDATE_DATEDAT ED_LAST_UPDATED_BYCHAR(10)

EDW_TIME_HIERARCHY

PID_for_DF_Fees

Users

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Types of SCD Implementation:

Type 1 Slowly Changing Dimension

In Type 1 Slowly Changing Dimension, the new information simply overwrites the original information. In other words, no history is kept.

In our example, recall we originally have the following table:

Customer Key Name State

1001 Christina Illinois

After Christina moved from Illinois to California, the new information replaces the new record, and we have the following table:


1001 Christina California

Advantages:

- This is the easiest way to handle the Slowly Changing Dimension problem, since there is no need to keep track of the old information.

Disadvantages:

- All history is lost. By applying this methodology, it is not possible to trace back in history. For example, in this case, the company would not be able to know that Christina lived in Illinois before.

- Usage :

About 50% of the time.

When to use Type 1:

Type 1 slowly changing dimension should be used when it is not necessary for the data warehouse to keep track of historical changes.


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In Type 2 Slowly Changing Dimension, a new record is added to the table to represent the new information. Therefore, both the original and the new record will be present. The newe record gets its own primary key.




After Christina moved from Illinois to California, we add the new information as a new row into the table:



1005 Christina California

Advantages:

- This allows us to accurately keep all historical information.

Disadvantages:

- This will cause the size of the table to grow fast. In cases where the number of rows for the table is very high to start with, storage and performance can become a concern.

- This necessarily complicates the ETL process.

Usage:

About 50% of the time.

When to use Type 2:

Type 2 slowly changing dimension should be used when it is necessary for the data warehouse to track historical changes.


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In Type 3 Slowly Changing Dimension, there will be two columns to indicate the particular attribute of interest, one indicating the original value, and one indicating the current value. There will also be a column that indicates when the current value becomes active.




To accommodate Type 3 Slowly Changing Dimension, we will now have the following columns:

Customer Key

Name

Original State

Current State

Effective Date

After Christina moved from Illinois to California, the original information gets updated, and we have the following table (assuming the effective date of change is January 15, 2003):

Customer Key Name Original State Current State Effective Date

1001 Christina Illinois California 15-JAN-2003

Advantages:

- This does not increase the size of the table, since new information is updated.

- This allows us to keep some part of history.

Disadvantages:

- Type 3 will not be able to keep all history where an attribute is changed more than once. For example, if Christina later moves to Texas on December 15, 2003, the California information will be lost.

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Usage:

Type 3 is rarely used in actual practice.

When to use Type 3:

Type III slowly changing dimension should only be used when it is necessary for the data warehouse to track historical changes, and when such changes will only occur for a finite number of time.

What is Staging area why we need it in DWH?

If target and source databases are different and target table volume is high it contains some millions of records in this scenario without staging table we need to design your informatica using look up to find out whether the record exists or not in the target table since target has huge volumes so its costly to create cache it will hit the performance.

If we create staging tables in the target database we can simply do outer join in the source qualifier to determine insert/update this approach will give you good performance.

It will avoid full table scan to determine insert/updates on target.And also we can create index on staging tables since these tables were designed for specific application it will not impact to any other schemas/users.

While processing flat files to data warehousing we can perform cleansing. Data cleansing, also known as data scrubbing, is the process of ensuring that a set of data is correct and accurate. During data cleansing, records are checked for accuracy and consistency.

Since it is one-to-one mapping from ODS to staging we do truncate and reload.

We can create indexes in the staging state, to perform our source qualifier best.

If we have the staging area no need to relay on the informatics transformation to known whether the record exists or not.

Data cleansing

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http://www.wisegeek.com/what-is-data-scrubbing.htm

Weeding out unnecessary or unwanted things (characters and spaces etc) from incoming data to make it more meaningful and informative

Data merging

Data can be gathered from heterogeneous systems and put together

Data scrubbing

Data scrubbing is the process of fixing or eliminating individual pieces of data that are incorrect, incomplete or duplicated before the data is passed to end user.

Data scrubbing is aimed at more than eliminating errors and redundancy. The goal is also to bring consistency to various data sets that may have been created with different, incompatible business rules.

ODS (Operational Data Sources):

My understanding of ODS is, its a replica of OLTP system and so the need of this, is to reduce the burden on production system (OLTP) while fetching data for loading targets. Hence its a mandate Requirement for every Warehouse.

So every day do we transfer data to ODS from OLTP to keep it up to date?

OLTP is a sensitive database they should not allow multiple select statements it may impact the performance as well as if something goes wrong while fetching data from OLTP to data warehouse it will directly impact the business.

ODS is the replication of OLTP.

ODS is usually getting refreshed through some oracle jobs.

enables management to gain a consistent picture of the business.

What is a surrogate key?

A surrogate key is a substitution for the natural primary key. It is a unique identifier or number ( normally created by a database sequence generator ) for each record of a dimension table that can be used for the primary key to the table.

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A surrogate key is useful because natural keys may change.

What is the difference between a primary key and a surrogate key?

A primary key is a special constraint on a column or set of columns. A primary key constraint ensures that the column(s) so designated have no NULL values, and that every value is unique. Physically, a primary key is implemented by the database system using a unique index, and all the columns in the primary key must have been declared NOT NULL. A table may have only one primary key, but it may be composite (consist of more than one column).

A surrogate key is any column or set of columns that can be declared as the primary key instead of a "real" or natural key. Sometimes there can be several natural keys that could be declared as the primary key, and these are all called candidate keys. So a surrogate is a candidate key. A table could actually have more than one surrogate key, although this would be unusual. The most common type of surrogate key is an incrementing integer, such as an auto increment column in MySQL, or a sequence in Oracle, or an identity column in SQL Server.

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4 ETL-INFORMATICA

4.1 Informatica Overview

Informatica is a powerful Extraction, Transformation, and Loading tool and is been deployed at GE Medical Systems for data warehouse development in the Business Intelligence Team. Informatica comes with the following clients to perform various tasks.

Designer – used to develop transformations/mappings Workflow Manager / Workflow Monitor replace the Server Manager -

used to create sessions / workflows/ worklets to run, schedule, and monitor mappings for data movement

Repository Manager – used to maintain folders, users, permissions, locks, and repositories.

Integration Services – the “workhorse” of the domain. Informatica Server is the component responsible for the actual work of moving data according to the mappings developed and placed into operation. It contains several distinct parts such as the Load Manager, Data Transformation Manager, Reader and Writer.

Repository Services- Informatica client tools and Informatica Server connect to the repository database over the network through the Repository Server.

Informatica Transformations:

Mapping: Mapping is the Informatica Object which contains set of transformations including source and target. Its look like pipeline.

Mapplet:

Mapplet is a set of reusable transformations. We can use this mapplet in any mapping within the Folder.

A mapplet can be active or passive depending on the transformations in the mapplet. Active mapplets contain one or more active transformations. Passive mapplets contain only passive transformations.

When you add transformations to a mapplet, keep the following restrictions in mind:

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If you use a Sequence Generator transformation, you must use a reusable Sequence Generator transformation.

If you use a Stored Procedure transformation, you must configure the Stored Procedure Type to be Normal.

You cannot include the following objects in a mapplet:

o Normalizer transformations

o COBOL sources

o XML Source Qualifier transformations

o XML sources

o Target definitions

o Other mapplets

The mapplet contains Input transformations and/or source definitions with at least one port connected to a transformation in the mapplet.

The mapplet contains at least one Output transformation with at least one port connected to a transformation in the mapplet.

Input Transformation: Input transformations are used to create a logical interface to a mapplet in order to allow data to pass into the mapplet.

Output Transformation: Output transformations are used to create a logical interface from a mapplet in order to allow data to pass out of a mapplet.

System Variables

$$$SessStartTime returns the initial system date value on the machine hosting the Integration Service when the server initializes a session. $$$SessStartTime returns the session start time as a string value. The format of the string depends on the database you are using.

Session: A session is a set of instructions that tells informatica Server how to move data from sources to targets.

WorkFlow: A workflow is a set of instructions that tells Informatica Server how to execute tasks such as sessions, email notifications and commands. In a

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workflow multiple sessions can be included to run in parallel or sequential manner.

Source Definition: The Source Definition is used to logically represent database table or Flat files.

Target Definition: The Target Definition is used to logically represent a database table or file in the Data Warehouse / Data Mart.

Aggregator: The Aggregator transformation is used to perform Aggregate calculations on group basis.

Expression: The Expression transformation is used to perform the arithmetic calculation on row by row basis and also used to convert string to integer vis and concatenate two columns.

Filter: The Filter transformation is used to filter the data based on single condition and pass through next transformation.

Router: The router transformation is used to route the data based on multiple conditions and pass through next transformations.

It has three groups

1) Input group

2) User defined group

3) Default group

Joiner: The Joiner transformation is used to join two sources residing in different databases or different locations like flat file and oracle sources or two relational tables existing in different databases.

Source Qualifier: The Source Qualifier transformation is used to describe in SQL the method by which data is to be retrieved from a source application system and also

used to join two relational sources residing in same databases.

What is Incremental Aggregation?

A. Whenever a session is created for a mapping Aggregate Transformation, the session option for Incremental Aggregation can be enabled. When

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PowerCenter performs incremental aggregation, it passes new source data through the mapping and uses historical cache data to perform new aggregation calculations incrementally.

Lookup: Lookup transformation is used in a mapping to look up data in a flat file or a relational table, view, or synonym.

Two types of lookups:

1) Connected

2) Unconnected

Differences between connected lookup and unconnected lookup

Connected Lookup Unconnected Lookup

This is connected to pipleline and receives the input values from pipleline.

Which is not connected to pipeline and receives input values from the result of a: LKP expression in another transformation via arguments.

We cannot use this lookup more than once in a mapping.

We can use this transformation more than once within the mapping

We can return multiple columns from the same row.

Designate one return port (R), returns one column from each row.

We can configure to use dynamic cache.

We cannot configure to use dynamic cache.

Pass multiple output values to another transformation. Link lookup/output ports to another transformation.

Pass one output value to another transformation. The lookup/output/return port passes the value to the transformation calling: LKP expression.

Use a dynamic or static cache Use a static cache

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values. default values.

Cache includes the lookup source column in the lookup condition and the lookup source columns that are output ports.

Cache includes all lookup/output ports in the lookup condition and the lookup/return port.

Lookup Caches:

When configuring a lookup cache, you can specify any of the following options:

Persistent cache

Recache from lookup source

Static cache

Dynamic cache

Shared cache

Dynamic cache: When you use a dynamic cache, the PowerCenter Server updates the lookup cache as it passes rows to the target.

If you configure a Lookup transformation to use a dynamic cache, you can only use the equality operator (=) in the lookup condition.

NewLookupRow Port will enable automatically.

NewLookupRow Value

Description

0The PowerCenter Server does not update or insert the row in the cache.

1 The PowerCenter Server inserts the row into the cache.

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2 The PowerCenter Server updates the row in the cache.

Static cache: It is a default cache; the PowerCenter Server doesn’t update the lookup cache as it passes rows to the target.

Persistent cache: If the lookup table does not change between sessions, configure the Lookup transformation to use a persistent lookup cache. The PowerCenter Server then saves and reuses cache files from session to session, eliminating the time required to read the lookup table.

Differences between dynamic lookup and static lookup

Dynamic Lookup Cache Static Lookup Cache

In dynamic lookup the cache memory will get refreshed as soon as the record get inserted or updated/deleted in the lookup table.

In static lookup the cache memory will not get refreshed even though record inserted or updated in the lookup table it will refresh only in the next session run.

When we configure a lookup transformation to use a dynamic lookup cache, you can only use the equality operator in the lookup condition.

NewLookupRow port will enable automatically.

It is a default cache.

Best example where we need to use dynamic cache is if suppose first record and last record both are same but there is a change in the address. What informatica mapping has to do here is first record needs to get insert and last record should get update in the target table.

If we use static lookup first record it will go to lookup and check in the lookup cache based on the condition it will not find the match so it will return null value then in the router it will send that record to insert flow.

But still this record dose not available in the cache memory so

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when the last record comes to lookup it will check in the cache it will not find the match so it returns null value again it will go to insert flow through router but it is suppose to go to update flow because cache didn’t get refreshed when the first record get inserted into target table.

Normalizer: The Normalizer transformation is used to generate multiple records from a single record based on columns (transpose the column data into rows)

We can use normalize transformation to process cobol sources instead of source qualifier.

Rank: The Rank transformation allows you to select only the top or bottom rank of data. You can use a Rank transformation to return the largest or smallest numeric value in a port or group.

The Designer automatically creates a RANKINDEX port for each Rank transformation.

Sequence Generator: The Sequence Generator transformation is used to generate numeric key values in sequential order.

Stored Procedure: The Stored Procedure transformation is used to execute externally stored database procedures and functions. It is used to perform the database level operations.

Sorter: The Sorter transformation is used to sort data in ascending or descending order according to a specified sort key. You can also configure the Sorter transformation for case-sensitive sorting, and specify whether the output rows should be distinct. The Sorter transformation is an active transformation. It must be connected to the data flow.

Union Transformation:

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The Union transformation is a multiple input group transformation that you can use to merge data from multiple pipelines or pipeline branches into one pipeline branch. It merges data from multiple sources similar to the UNION ALL SQL statement to combine the results from two or more SQL statements. Similar to the UNION ALL statement, the Union transformation does not remove duplicate rows.Input groups should have similar structure.

Update Strategy: The Update Strategy transformation is used to indicate the DML statement.

We can implement update strategy in two levels:

1) Mapping level

2) Session level.

Session level properties will override the mapping level properties.

Aggregator Transformation:

Transformation type:

Active

Connected

The Aggregator transformation performs aggregate calculations, such as averages and sums. The Aggregator transformation is unlike the Expression transformation, in that you use the Aggregator transformation to perform calculations on groups. The Expression transformation permits you to perform calculations on a row-by-row basis only.

Components of the Aggregator Transformation:

The Aggregator is an active transformation, changing the number of rows in the pipeline. The Aggregator transformation has the following components and options

Aggregate cache: The Integration Service stores data in the aggregate cache until it completes aggregate calculations. It stores group values in an index

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cache and row data in the data cache.

Group by port: Indicate how to create groups. The port can be any input, input/output, output, or variable port. When grouping data, the Aggregator transformation outputs the last row of each group unless otherwise specified.

Sorted input: Select this option to improve session performance. To use sorted input, you must pass data to the Aggregator transformation sorted by group by port, in ascending or descending order.

Aggregate Expressions:

The Designer allows aggregate expressions only in the Aggregator transformation. An aggregate expression can include conditional clauses and non-aggregate functions. It can also include one aggregate function nested within another aggregate function, such as:

MAX (COUNT (ITEM))

The result of an aggregate expression varies depending on the group by ports used in the transformation

Aggregate Functions

Use the following aggregate functions within an Aggregator transformation. You can nest one aggregate function within another aggregate function.

The transformation language includes the following aggregate functions:

(AVG,COUNT,FIRST,LAST,MAX,MEDIAN,MIN,PERCENTAGE,SUM,VARIANCE and STDDEV)

When you use any of these functions, you must use them in an expression within an Aggregator transformation.

Perfomance Tips in Aggregator

Use sorted input to increase the mapping performance but we need to sort the data before sending to aggregator transformation.

Filter the data before aggregating it.

If you use a Filter transformation in the mapping, place the transformation before the Aggregator transformation to reduce unnecessary aggregation.

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SQL Transformation


Active/Passive

Connected

The SQL transformation processes SQL queries midstream in a pipeline. You can insert, delete, update, and retrieve rows from a database. You can pass the database connection information to the SQL transformation as input data at run time. The transformation processes external SQL scripts or SQL queries that you create in an SQL editor. The SQL transformation processes the query and returns rows and database errors.

For example, you might need to create database tables before adding new transactions. You can create an SQL transformation to create the tables in a workflow. The SQL transformation returns database errors in an output port. You can configure another workflow to run if the SQL transformation returns no errors.

When you create an SQL transformation, you configure the following options:

Mode. The SQL transformation runs in one of the following modes:

Script mode. The SQL transformation runs ANSI SQL scripts that are externally located. You pass a script name to the transformation with each input row. The SQL transformation outputs one row for each input row.

Query mode. The SQL transformation executes a query that you define in a query editor. You can pass strings or parameters to the query to define dynamic queries or change the selection parameters. You can output multiple rows when the query has a SELECT statement.

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Database type. The type of database the SQL transformation connects to.

Connection type. Pass database connection information to the SQL transformation or use a connection object.

Script Mode

An SQL transformation configured for script mode has the following default ports:

Port Type Description

ScriptName Input Receives the name of the script to execute for the current row.

ScriptResult Output Returns PASSED if the script execution succeeds for the row. Otherwise contains FAILED.

ScriptError Output Returns errors that occur when a script fails for a row.

Java Transformation Overview


Active/Passive

Connected

The Java transformation provides a simple native programming interface to define transformation functionality with the Java programming language. You can use the Java transformation to quickly define simple or moderately complex transformation functionality without advanced knowledge of the Java programming language or an external Java development environment.

For example, you can define transformation logic to loop through input rows and generate multiple output rows based on a specific condition. You can also use expressions, user-defined functions, unconnected transformations, and mapping variables in the Java code.

Transaction Control Transformation

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Active

Connected

PowerCenter lets you control commit and roll back transactions based on a set of rows that pass through a Transaction Control transformation. A transaction is the set of rows bound by commit or roll back rows. You can define a transaction based on a varying number of input rows. You might want to define transactions based on a group of rows ordered on a common key, such as employee ID or order entry date.

In PowerCenter, you define transaction control at the following levels:

Within a mapping. Within a mapping, you use the Transaction Control transformation to define a transaction. You define transactions using an expression in a Transaction Control transformation. Based on the return value of the expression, you can choose to commit, roll back, or continue without any transaction changes.

Within a session. When you configure a session, you configure it for user-defined commit. You can choose to commit or roll back a transaction if the Integration Service fails to transform or write any row to the target.

When you run the session, the Integration Service evaluates the expression for each row that enters the transformation. When it evaluates a commit row, it commits all rows in the transaction to the target or targets. When the Integration Service evaluates a roll back row, it rolls back all rows in the transaction from the target or targets.

If the mapping has a flat file target you can generate an output file each time the Integration Service starts a new transaction. You can dynamically name each target flat file.

What is the difference between joiner and lookup

Joiner Lookup

In joiner on multiple matches it will return all matching records.

In lookup it will return either first record or last record or any value or error value.

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In joiner we cannot configure to use persistence cache, shared cache, uncached and dynamic cache

Where as in lookup we can configure to use persistence cache, shared cache, uncached and dynamic cache.

We cannot override the query in joiner

We can override the query in lookup to fetch the data from multiple tables.

We can perform outer join in joiner transformation.

We cannot perform outer join in lookup transformation.

We cannot use relational operators in joiner transformation.(i.e. <,>,<= and so on)

Where as in lookup we can use the relation operators. (i.e. <,>,<= and so on)

What is the difference between source qualifier and lookup

Source Qualifier Lookup

In source qualifier it will push all the matching records.

Where as in lookup we can restrict whether to display first value, last value or any value

In source qualifier there is no concept of cache.

Where as in lookup we concentrate on cache concept.

When both source and lookup are in same database we can use source qualifier.

When the source and lookup table exists in different database then we need to use lookup.

Have you done any Performance tuning in informatica?

1) Yes, One of my mapping was taking 3-4 hours to process 40 millions rows into staging table we don’t have any transformation inside the mapping its 1 to 1 mapping .Here nothing is there to optimize the mapping so I created session partitions using key range on effective date column. It improved performance lot, rather than 4 hours it was running in 30 minutes for entire 40millions.Using partitions DTM will creates multiple reader and writer threads.

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2) There was one more scenario where I got very good performance in the mapping level .Rather than using lookup transformation if we can able to do outer join in the source qualifier query override this will give you good performance if both lookup table and source were in the same database. If lookup tables is huge volumes then creating cache is costly.

3) And also if we can able to optimize mapping using less no of transformations always gives you good performance.

4) If any mapping taking long time to execute then first we need to look in to source and target statistics in the monitor for the throughput and also find out where exactly the bottle neck by looking busy percentage in the session log will come to know which transformation taking more time ,if your source query is the bottle neck then it will show in the end of the session log as “query issued to database “that means there is a performance issue in the source query.we need to tune the query using .

Informatica Session Log shows busy percentage

If we look into session logs it shows busy percentage based on that we need to find out where is bottle neck.

***** RUN INFO FOR TGT LOAD ORDER GROUP [1], CONCURRENT SET [1] ****

Thread [READER_1_1_1] created for [the read stage] of partition point [SQ_ACW_PCBA_APPROVAL_STG] has completed: Total Run Time = [7.193083] secs, Total Idle Time = [0.000000] secs, Busy Percentage = [100.000000]

Thread [TRANSF_1_1_1] created for [the transformation stage] of partition point [SQ_ACW_PCBA_APPROVAL_STG] has completed. The total run time was insufficient for any meaningful statistics.

Thread [WRITER_1_*_1] created for [the write stage] of partition point [ACW_PCBA_APPROVAL_F1, ACW_PCBA_APPROVAL_F] has completed: Total Run Time = [0.806521] secs, Total Idle Time = [0.000000] secs, Busy Percentage = [100.000000]

If suppose I've to load 40 lacs records in the target table and the workflow is taking about 10 - 11 hours to finish. I've already increased the cache size to 128MB.

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http://datawarehouse.ittoolbox.com/groups/technical-functional/informatica-l/performance-tuning-of-informatica-mapping-1227895#%23

There are no joiner, just lookups and expression transformations

Ans:

(1) If the lookups have many records, try creating indexes on the columns used in the lkp condition. And try increasing the lookup cache.If this doesnt increase the performance. If the target has any indexes disable them in the target pre load and enable them in the target post load.

(2) Three things you can do w.r.t it.

1. Increase the Commit intervals ( by default its 10000) 2. Use bulk mode instead of normal mode incase ur target doesn't have primary keys or use pre and post session SQL to implement the same (depending on the business req.) 3. Uses Key partitionning to load the data faster.

(3)If your target consists key constraints and indexes u slow

the loading of data. To improve the session performance in

this case drop constraints and indexes before you run the

session and rebuild them after completion of session.

What is Constraint based loading in informatica?

By setting Constraint Based Loading property at session level in Configaration tab we can load the data into parent and child relational tables (primary foreign key).

Genarally What it do is it will load the data first in parent table then it will load it in to child table.

What is use of Shortcuts in informatica?

If we copy source definaltions or target definations or mapplets from Shared folder to any other folders that will become a shortcut.

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Let’s assume we have imported some source and target definitions in a shared folder after that we are using those sources and target definitions in another folders as a shortcut in some mappings.

If any modifications occur in the backend (Database) structure like adding new columns or drop existing columns either in source or target I f we reimport into shared folder those new changes automatically it would reflect in all folder/mappings wherever we used those sources or target definitions.

Target Update Override

If we don’t have primary key on target table using Target Update Override option we can perform updates.By default, the Integration Service updates target tables based on key values. However, you can override the default UPDATE statement for each target in a mapping. You might want to update the target based on non-key columns.

Overriding the WHERE Clause

You can override the WHERE clause to include non-key columns. For example, you might want to update records for employees named Mike Smith only. To do this, you edit the WHERE clause as follows:

UPDATE T_SALES SET DATE_SHIPPED =:TU.DATE_SHIPPED,TOTAL_SALES = :TU.TOTAL_SALES WHERE EMP_NAME = :TU.EMP_NAME and EMP_NAME = 'MIKE SMITH'

If you modify the UPDATE portion of the statement, be sure to use :TU to specify ports.

How do you perform incremental logic or Delta or CDC?

Incremental means suppose today we processed 100 records ,for tomorrow run u need to extract whatever the records inserted newly and updated after previous run based on last updated timestamp (Yesterday run) this process called as incremental or delta.

Approach_1: Using set max var ()

1) First need to create mapping var ($$Pre_sess_max_upd)and assign initial value as old date (01/01/1940).

2) Then override source qualifier query to fetch only LAT_UPD_DATE >=$

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$Pre_sess_max_upd (Mapping var)

3) In the expression assign max last_upd_date value to $$Pre_sess_max_upd(mapping var) using set max var

4) Because its var so it stores the max last upd_date value in the repository, in the next run our source qualifier query will fetch only the records updated or inseted after previous run.

Approach_2: Using parameter file

1 First need to create mapping parameter ($$Pre_sess_start_tmst )and assign initial value as old date (01/01/1940) in the parameterfile.

2 Then override source qualifier query to fetch only LAT_UPD_DATE >=$$Pre_sess_start_tmst (Mapping var)

3 Update mapping parameter($$Pre_sess_start_tmst) values in the parameter file using shell script or another mapping after first session get completed successfully

4 Because its mapping parameter so every time we need to update the value in the parameter file after comptetion of main session.

Approach_3: Using oracle Control tables

1 First we need to create two control tables cont_tbl_1 and cont_tbl_1 with structure of session_st_time,wf_name

2 Then insert one record in each table with session_st_time=1/1/1940 and workflow_name

3 create two store procedures one for update cont_tbl_1 with session st_time, set property of store procedure type as Source_pre_load .

4 In 2nd store procedure set property of store procedure type as Target _Post_load.this proc will update the session _st_time in Cont_tbl_2 from cnt_tbl_1.

5 Then override source qualifier query to fetch only LAT_UPD_DATE >=(Select session_st_time from cont_tbl_2 where workflow

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name=’Actual work flow name’.

SCD Type-II Effective-Date Approach

We have one of the dimension in current project called resource dimension. Here we are maintaining the history to keep track of SCD changes.

To maintain the history in slowly changing dimension or resource dimension. We followed SCD Type-II Effective-Date approach.

My resource dimension structure would be eff-start-date, eff-end-date, s.k and source columns.

Whenever I do a insert into dimension I would populate eff-start-date with sysdate, eff-end-date with future date and s.k as a sequence number.

If the record already present in my dimension but there is change in the source data. In that case what I need to do is

Update the previous record eff-end-date with sysdate and insert as a new record with source data.

Informatica design to implement SDC Type-II effective-date approach

Once you fetch the record from source qualifier. We will send it to lookup to find out whether the record is present in the target or not based on source primary key column.

Once we find the match in the lookup we are taking SCD column from lookup and source columns from SQ to expression transformation.

In lookup transformation we need to override the lookup override query to fetch Active records from the dimension while building the cache.

In expression transformation I can compare source with lookup return data.

If the source and target data is same then I can make a flag as ‘S’.

If the source and target data is different then I can make a flag as ‘U’.

If source data does not exists in the target that means lookup returns null

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value. I can flag it as ‘I’.

Based on the flag values in router I can route the data into insert and update flow.

If flag=’I’ or ‘U’ I will pass it to insert flow.

If flag=’U’ I will pass this record to eff-date update flow

When we do insert we are passing the sequence value to s.k.

Whenever we do update we are updating the eff-end-date column based on lookup return s.k value.

Complex Mapping

We have one of the order file requirement. Requirement is every day in source system they will place filename with timestamp in informatica server.

We have to process the same date file through informatica.

Source file directory contain older than 30 days files with timestamps.

For this requirement if I hardcode the timestamp for source file name it will process the same file every day.

So what I did here is I created $InputFilename for source file name.

Then I am going to use the parameter file to supply the values to session variables ($InputFilename).

To update this parameter file I have created one more mapping.

This mapping will update the parameter file with appended timestamp to file name.

I make sure to run this parameter file update mapping before my actual mapping.

How to handle errors in informatica?

We have one of the source with numerator and denominator values we need to calculate num/deno

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When populating to target.

If deno=0 I should not load this record into target table.

We need to send those records to flat file after completion of 1st session run. Shell script will check the file size.

If the file size is greater than zero then it will send email notification to source system POC (point of contact) along with deno zero record file and appropriate email subject and body.

If file size<=0 that means there is no records in flat file. In this case shell script will not send any email notification.

Or

We are expecting a not null value for one of the source column.

If it is null that means it is a error record.

We can use the above approach for error handling.

Why we need source qualifier?

Simply it performs select statement.

Select statement fetches the data in the form of row.

Source qualifier will select the data from the source table.

It identifies the record from the source.

Parameter file it will supply the values to session level variables and mapping level variables.

Variables are of two types:

Session level variables

Mapping level variables

Session level variables are of four types:

$DBConnection_Source

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$DBConnection_Target

$InputFile

$OutputFile

Mapping level variables are of two types:

Variable

Parameter

What is the difference between mapping level and session level variables?

Mapping level variables always starts with $$.

A session level variable always starts with $.

Flat File

Flat file is a collection of data in a file in the specific format.

Informatica can support two types of files

Delimiter

Fixed Width

In delimiter we need to specify the separator.

In fixed width we need to known about the format first. Means how many character to read for particular column.

In delimiter also it is necessary to know about the structure of the delimiter. Because to know about the headers.

If the file contains the header then in definition we need to skip the first row.

List file:

If you want to process multiple files with same structure. We don’t need multiple mapping and multiple sessions.

We can use one mapping one session using list file option. of 112


First we need to create the list file for all the files. Then we can use this file in the main mapping.

Parameter file Format:

It is a text file below is the format for parameter file. We use to place this file in the unix box where we have installed our informatic server.

[GEHC_APO_DEV.WF:w_GEHC_APO_WEEKLY_HIST_LOAD.WT:wl_GEHC_APO_WEEKLY_HIST_BAAN.ST:s_m_GEHC_APO_BAAN_SALES_HIST_AUSTRI]

$InputFileName_BAAN_SALE_HIST=/interface/dev/etl/apo/srcfiles/HS_025_20070921

$DBConnection_Target=DMD2_GEMS_ETL

$$CountryCode=AT

$$CustomerNumber=120165

[GEHC_APO_DEV.WF:w_GEHC_APO_WEEKLY_HIST_LOAD.WT:wl_GEHC_APO_WEEKLY_HIST_BAAN.ST:s_m_GEHC_APO_BAAN_SALES_HIST_BELUM]

$DBConnection_Sourcet=DEVL1C1_GEMS_ETL

$OutputFileName_BAAN_SALES=/interface/dev/etl/apo/trgfiles/HS_002_20070921

$$CountryCode=BE

$$CustomerNumber=101495

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Difference between 7.x and 8.x

Power Center 7.X Architecture.

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Power Center 8.X Architecture.

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Developer Changes:

For example, in PowerCenter:

• PowerCenter Server has become a service, the Integration Service

• No more Repository Server, but PowerCenter includes a Repository Service

• Client applications are the same, but work on top of the new services framework

Below are the difference between 7.1 and 8.1 of infa..

1) powercenter connect for sap netweaver bw option

2) sql transformation is added

3) service oriented architecture

4) grid concept is additional feature

5) random file name can genaratation in target

6) command line programms: infacmd and infasetup new commands were added.

7) java transformation is added feature

8) concurrent cache creation and faster index building are additional feature in lookup transformation

9) caches or automatic u dont need to allocate at transformation level

10) push down optimization techniques,some

11) we can append data into the flat file target.

12)Dynamic file names we can generate in informatica 8

13)flat file names we can populate to target while processing through list file .

14)For Falt files header and footer we can populate using advanced options in 8 at session level.

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15) GRID option at session level

Effective in version 8.0, you create and configure a grid in the Administration Console. You configure a grid to run on multiple nodes, and you configure one Integration Service to run on the grid. The Integration Service runs processes on the nodes in the grid to distribute workflows and sessions. In addition to running a workflow on a grid, you can now run a session on a grid. When you run a session or workflow on a grid, one service process runs on each available node in the grid.

Pictorial Representation of Workflow execution:

1. A PowerCenter Client request IS to start workflow

2. IS starts ISP

3. ISP consults LB to select node

4. ISP starts DTM in node selected by LB

Integration Service (IS)

The key functions of IS are

Interpretation of the workflow and mapping metadata from the repository.

Execution of the instructions in the metadata

Manages the data from source system to target system within the of 112


memory and disk

The main three components of Integration Service which enable data movement are,

Integration Service Process

Load Balancer

Data Transformation Manager

Integration Service Process (ISP)

The Integration Service starts one or more Integration Service processes to run and monitor workflows. When we run a workflow, the ISP starts and locks the workflow, runs the workflow tasks, and starts the process to run sessions. The functions of the Integration Service Process are,

Locks and reads the workflow

Manages workflow scheduling, ie, maintains session dependency

Reads the workflow parameter file

Creates the workflow log

Runs workflow tasks and evaluates the conditional links

Starts the DTM process to run the session

Writes historical run information to the repository

Sends post-session emails

Load Balancer

The Load Balancer dispatches tasks to achieve optimal performance. It dispatches tasks to a single node or across the nodes in a grid after performing a sequence of steps. Before understanding these steps we have to know about Resources, Resource Provision Thresholds, Dispatch mode and Service levels

Resources – we can configure the Integration Service to check the resources available on each node and match them with the resources required to run the task. For example, if a session uses an SAP source, the

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Load Balancer dispatches the session only to nodes where the SAP client is installed

Three Resource Provision Thresholds, The maximum number of runnable threads waiting for CPU resources on the node called Maximum CPU Run Queue Length. The maximum percentage of virtual memory allocated on the node relative to the total physical memory size called Maximum Memory %. The maximum number of running Session and Command tasks allowed for each Integration Service process running on the node called Maximum Processes

Three Dispatch mode’s – Round-Robin: The Load Balancer dispatches tasks to available nodes in a round-robin fashion after checking the “Maximum Process” threshold. Metric-based: Checks all the three resource provision thresholds and dispatches tasks in round robin fashion. Adaptive: Checks all the three resource provision thresholds and also ranks nodes according to current CPU availability

Service Levels establishes priority among tasks that are waiting to be dispatched, the three components of service levels are Name, Dispatch Priority and Maximum dispatch wait time. “Maximum dispatch wait time” is the amount of time a task can wait in queue and this ensures no task waits forever

A .Dispatching Tasks on a node

1. The Load Balancer checks different resource provision thresholds on the node depending on the Dispatch mode set. If dispatching the task causes any threshold to be exceeded, the Load Balancer places the task in the dispatch queue, and it dispatches the task later

2. The Load Balancer dispatches all tasks to the node that runs the master Integration Service process

B. Dispatching Tasks on a grid,

1. The Load Balancer verifies which nodes are currently running and enabled

2. The Load Balancer identifies nodes that have the PowerCenter resources required by the tasks in the workflow

3. The Load Balancer verifies that the resource provision thresholds on each

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candidate node are not exceeded. If dispatching the task causes a threshold to be exceeded, the Load Balancer places the task in the dispatch queue, and it dispatches the task later

4. The Load Balancer selects a node based on the dispatch mode

Data Transformation Manager (DTM) Process

When the workflow reaches a session, the Integration Service Process starts the DTM process. The DTM is the process associated with the session task. The DTM process performs the following tasks:

Retrieves and validates session information from the repository.

Validates source and target code pages.

Verifies connection object permissions.

Performs pushdown optimization when the session is configured for pushdown optimization.

Adds partitions to the session when the session is configured for dynamic partitioning.

Expands the service process variables, session parameters, and mapping variables and parameters.

Creates the session log.

Runs pre-session shell commands, stored procedures, and SQL.

Sends a request to start worker DTM processes on other nodes when the session is configured to run on a grid.

Creates and runs mapping, reader, writer, and transformation threads to extract, transform, and load data

Runs post-session stored procedures, SQL, and shell commands and sends post-session email

After the session is complete, reports execution result to ISP

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Approach_1: Using set max var ()

1) First need to create mapping var ($$INCREMENT_TS)and assign initial

value as old date (01/01/1940).

2) Then override source qualifier query to fetch only LAT_UPD_DATE >=($

$INCREMENT_TS (Mapping var)

3) In the expression assign max last_upd_date value to ($

$INCREMENT_TS (mapping var) using set max var

4) Because its var so it stores the max last upd_date value in the

repository, in the next run our source qualifier query will fetch only the

records updated or inseted after previous run.

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Logic in the mapping variable is

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Logic in the SQ is

In expression assign max last update date value to the variable using function set max variable.

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Logic in the update strategy is below

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Approach_2: Using parameter file

First need to create mapping parameter ($$LastUpdateDate Time )and assign initial value as old date (01/01/1940) in the parameterfile.

Then override source qualifier query to fetch only LAT_UPD_DATE >=($$LastUpdateDate Time (Mapping var)

Update mapping parameter($$LastUpdateDate Time) values in the parameter file using shell script or another mapping after first session get completed successfully

Because its mapping parameter so every time we need to update the value in the parameter file after comptetion of main session.

Parameterfile:

[GEHC_APO_DEV.WF:w_GEHC_APO_WEEKLY_HIST_LOAD.WT:wl_GEHC_APO_WEEKLY_HIST_BAAN.ST:s_m_GEHC_APO_BAAN_SALES_HIST_AUSTRI]

$DBConnection_Source=DMD2_GEMS_ETL

$DBConnection_Target=DMD2_GEMS_ETL

$$LastUpdateDate Time =01/01/1940

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Updating parameter File

Logic in the expression

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Main mapping

Sql override in SQ Transformation

Workflod Design

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4.2 Informatica Scenarios:

1) How to populate 1st record to 1st target ,2nd record to 2nd target ,3rd record to 3rd target and 4th record to 1st target through informatica?

We can do using sequence generator by setting end value=3 and enable cycle option.then in the router take 3 goups

In 1st group specify condition as seq next value=1 pass those records to 1st target simillarly

In 2nd group specify condition as seq next value=2 pass those records to 2nd target

In 3rd group specify condition as seq next value=3 pass those records to 3rd target.

Since we have enabled cycle option after reaching end value sequence generator will start from 1,for the 4th record seq.next value is 1 so it will go to

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1st target.

2) How to do Dymanic File generation in Informatica?

I want to generate the separate file for every State (as per state, it should generate file).It has to generate 2 flat files and name of the flat file is corresponding state name that is the requirement.

Below is my mapping.

Source (Table) -> SQ -> Target (FF)

Source:

State Transaction City

AP 2 HYD

AP 1 TPT

KA 5 BANG

KA 7 MYSORE

KA 3 HUBLI

This functionality was added in informatica 8.5 onwards earlier versions it was not there.

We can achieve it with use of transaction control and special "FileName" port in the target file .

In order to generate the target file names from the mapping, we should make use of the special "FileName" port in the target file. You can't create this special port from the usual New port button. There is a special button with label "F" on it to the right most corner of the target flat file when viewed in "Target Designer".

When you have different sets of input data with different target files created, use the same instance, but with a Transaction Control transformation which defines the boundary for the source sets.

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in target flat file there is option in column tab i.e filename as column.when you click that one non editable column gets created in metadata of target.

in transaction control give condition as iif(not isnull(emp_no),tc_commit_before,continue) else tc_commit_before

map the emp_no column to target's filename column

ur mapping will be like this

source -> squlf-> transaction control-> target

run it ,separate files will be created by name of Ename

3) How to concatenate row data through informatica?

Source:

Ename EmpNo

stev 100

methew 100

john 101

tom 101

Target:

Ename EmpNo

Stev methew

100

John tom 101

Approach1: Using Dynamic Lookup on Target table:

If record doen’t exit do insert in target .If it is already exist then get of 112


corresponding Ename vale from lookup and concat in expression with current Ename value then update the target Ename column using update strategy.

Approch2: Using Var port :

Sort the data in sq based on EmpNo column then Use expression to store previous record information using Var port after that use router to insert a record if it is first time if it is already inserted then update Ename with concat value of prev name and current name value then update in target.

4) How to send Unique (Distinct) records into One target and duplicates into another tatget?

Source:

Ename EmpNo

stev 100

Stev 100

john 101

Mathew 102

Output:

Target_1:

Ename EmpNo

Stev 100

John 101

Mathew 102

Target_2:

Ename EmpNo of 112


Stev 100

Approch 1: Using Dynamic Lookup on Target table:

If record doen’t exit do insert in target_1 .If it is already exist then send it to Target_2 using Router.

Approch2: Using Var port :

Sort the data in sq based on EmpNo column then Use expression to store previous record information using Var ports after that use router to route the data into targets if it is first time then sent it to first target if it is already inserted then send it to Tartget_2.

5) How to Process multiple flat files to single target table through informatica if all files are same structure?

We can process all flat files through one mapping and one session using list file.

First we need to create list file using unix script for all flat file the extension of the list file is .LST.

This list file it will have only flat file names.

At session level we need to set

source file directory as list file path

And source file name as list file name

And file type as indirect.

6) How to populate file name to target while loading multiple files using list file concept.

In informatica 8.6 by selecting Add currently processed flatfile name option in the properties tab of source definition after import source file defination in source analyzer.It will add new column as currently processed file name.we

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can map this column to target to populate filename.

7) If we want to run 2 workflow one after another(how to set the dependence between wf’s)

If both workflow exists in same folder we can create 2 worklet rather than creating 2 workfolws.

Finally we can call these 2 worklets in one workflow.

There we can set the dependency.

If both workflows exists in different folders or repository then we cannot create worklet.

We can set the dependency between these two workflow using shell script is one approach.

The other approach is event wait and event rise.

If both workflow exists in different folrder or different rep then we can use below approaches.

1) Using shell script

As soon as first workflow get completes we are creating zero byte file (indicator file).

If indicator file is available in particular location. We will run second workflow.

If indicator file is not available we will wait for 5 minutes and again we will check for the indicator. Like this we will continue the loop for 5 times i.e 30 minutes.

After 30 minutes if the file does not exists we will send out email notification.

2) Event wait and Event rise approach

We can put event wait before actual session run in the workflow to wait a indicator file if file available then it will run the session other event wait it will wait for infinite time till the indicator file is available.

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8) How to load cumulative salary in to target ?

Solution:

Using var ports in expression we can load cumulative salary into target.

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4.3 Development Guidelines

General Development Guidelines

The starting point of the development is the logical model created by the Data Architect. This logical model forms the foundation for metadata, which will be continuously be maintained throughout the Data Warehouse Development Life Cycle (DWDLC). The logical model is formed from the requirements of the project. At the completion of the logical model technical documentation defining the sources, targets, requisite business rule transformations, mappings and filters. This documentation serves as the basis for the creation of the Extraction, Transformation and Loading tools to actually manipulate the data from the applications sources into the Data Warehouse/Data Mart.

To start development on any data mart you should have the following things set up by the Informatica Load Administrator

Informatica Folder. The development team in consultation with the BI Support Group can decide a three-letter code for the project, which would be used to create the informatica folder as well as Unix directory structure.

Informatica Userids for the developers of 112


Unix directory structure for the data mart. A schema XXXLOAD on DWDEV database.

Transformation Specifications

Before developing the mappings you need to prepare the specifications document for the mappings you need to develop. A good template is placed in the templates folder You can use your own template as long as it has as much detail or more than that which is in this template.

While estimating the time required to develop mappings the thumb rule is as follows.

Simple Mapping – 1 Person Day Medium Complexity Mapping – 3 Person Days Complex Mapping – 5 Person Days.

Usually the mapping for the fact table is most complex and should be allotted as much time for development as possible.

Data Loading from Flat Files

It’s an accepted best practice to always load a flat file into a staging table before any transformations are done on the data in the flat file.

Always use LTRIM, RTRIM functions on string columns before loading data into a stage table.

You can also use UPPER function on string columns but before using it you need to ensure that the data is not case sensitive (e.g. ABC is different from Abc)

If you are loading data from a delimited file then make sure the delimiter is not a character which could appear in the data itself. Avoid using comma-separated files. Tilde (~) is a good delimiter to use.

Failure Notification

Once in production your sessions and batches need to send out notification when then fail to the Support team. You can do this by configuring email task in the session level.

Naming Conventions and usage of Transformations of 112


Port Standards:

Input Ports – It will be necessary to change the name of input ports for lookups, expression and filters where ports might have the same name. If ports do have the same name then will be defaulted to having a number after the name. Change this default to a prefix of “in_”. This will allow you to keep track of input ports through out your mappings.Prefixed with: IN_

Variable Ports – Variable ports that are created within an expression

Transformation should be prefixed with a “v_”. This will allow the developer to distinguish between input/output and variable ports. For more explanation of Variable Ports see the section “VARIABLES”.Prefixed with: V_

Output Ports – If organic data is created with a transformation that will be mapped to the target, make sure that it has the same name as the target port that it will be mapped to.

Prefixed with: O_

Quick Reference

Object Type Syntax

Folder XXX_<Data Mart Name>

Mapping m_fXY_ZZZ_<Target Table Name>_x.x

Session s_fXY_ZZZ_<Target Table Name>_x.x

Batch b_<Meaningful name representing

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the sessions inside>

Source Definition <Source Table Name>

Target Definition <Target Table Name>

Aggregator AGG_<Purpose>

Expression EXP_<Purpose>

Filter FLT_<Purpose>

Joiner JNR_<Names of Joined Tables>

Lookup LKP_<Lookup Table Name>

Normalizer Norm_<Source Name>

Rank RNK_<Purpose>

Router RTR_<Purpose>

Sequence Generator SEQ_<Target Column Name>

Source Qualifier SQ_<Source Table Name>

Stored Procedure STP_<Database Name>_<Procedure Name>

Update Strategy UPD_<Target Table Name>_xxx

Mapplet MPP_<Purpose>

Input Transformation INP_<Description of Data being funneled in>

Output Tranformation OUT_<Description of Data being funneled out>

Database Connections XXX_<Database Name>_<Schema Name>

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4.4 Performance Tips

What is Performance tuning in Informatica

The aim of performance tuning is optimize session

performance so sessions run during the available load window

for the Informatica Server.

Increase the session performance by following.

The performance of the Informatica Server is related to

network connections. Data generally moves across a network

at less than 1 MB per second, whereas a local disk moves

data five to twenty times faster. Thus network connections

ofteny affect on session performance. So avoid work

connections.

1. Cache lookups if source table is under 500,000 rows and DON’T cache for tables over 500,000 rows.

2. Reduce the number of transformations. Don’t use an Expression Transformation to collect fields. Don’t use an Update Transformation if only inserting. Insert mode is the default.

3. If a value is used in multiple ports, calculate the value once (in a variable) and reuse the result instead of recalculating it for multiple ports.

4. Reuse objects where possible.

5. Delete unused ports particularly in the Source Qualifier and Lookups.

6. Use Operators in expressions over the use of functions.

7. Avoid using Stored Procedures, and call them only once during the mapping if possible.

8. Remember to turn off Verbose logging after you have finished debugging.

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9. Use default values where possible instead of using IIF (ISNULL(X),,) in Expression port.

10.When overriding the Lookup SQL, always ensure to put a valid Order By statement in the SQL. This will cause the database to perform the order rather than Informatica Server while building the Cache.

11.Improve session performance by using sorted data with the Joiner transformation. When the Joiner transformation is configured to use sorted data, the Informatica Server improves performance by minimizing disk input and output.

12.Improve session performance by using sorted input with the Aggregator Transformation since it reduces the amount of data cached during the session.

13.Improve session performance by using limited number of connected input/output or output ports to reduce the amount of data the Aggregator transformation stores in the data cache.

14. Use a Filter transformation prior to Aggregator transformation to reduce unnecessary aggregation.

15. Performing a join in a database is faster than performing join in the session. Also use the Source Qualifier to perform the join.

16.Define the source with less number of rows and master source in Joiner Transformations, since this reduces the search time and also the cache.

17.When using multiple conditions in a lookup conditions, specify the conditions with the equality operator first.

18.Improve session performance by caching small lookup tables.

19.If the lookup table is on the same database as the source table, instead of using a Lookup transformation, join the tables in the Source Qualifier Transformation itself if possible.

20.If the lookup table does not change between sessions, configure the Lookup transformation to use a persistent lookup cache. The Informatica Server saves and reuses cache files from session to session, eliminating the time required to read the lookup table.

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21.Use :LKP reference qualifier in expressions only when calling unconnected Lookup Transformations.

22.Informatica Server generates an ORDER BY statement for a cached lookup that contains all lookup ports. By providing an override ORDER BY clause with fewer columns, session performance can be improved.

23.Eliminate unnecessary data type conversions from mappings.

24.Reduce the number of rows being cached by using the Lookup SQL Override option to add a WHERE clause to the default SQL statement.

4.5 Unit Test Cases (UTP):

QA Life Cycle consists of 5 types of

Testing regimens:

1. Unit Testing

2. Functional Testing

3. System Integration Testing

4. User Acceptance Testing

Unit testing: The testing, by development, of the application modules to verify each unit (module) itself meets the accepted user requirements and design and development standards

Functional Testing: The testing of all the application’s modules individually to ensure the modules, as released from development to QA, work together as designed and meet the accepted user requirements and system standards

System Integration Testing: Testing of all of the application modules in the same environment, database instance, network and inter-related applications, as it would function in production. This includes security, volume and stress testing.

User Acceptance Testing(UAT): The testing of the entire application by the

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end-users ensuring the application functions as set forth in the system requirements documents and that the system meets the business needs.

UTP Template:

Step

#

Description Test Conditions Expected Results

Actual Results,

Pass or Fail

(P or F)

Tested By

SAP- CMS Interfaces

1 Check for the total count of records in source tables that is fetched and the total records in the PRCHG table for a perticular session timestamp

SOURCE:

SELECT count(*) FROM XST_PRCHG_STG

TARGET:

Select count(*) from _PRCHG

Both the source and target table load record count should match.

Should be same as the expected

Pass Stev

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Step

#


Actual Results,

Pass or Fail

(P or F)

Tested By

2 Check for all the target columns whether they are getting populated correctly with source data.

select PRCHG_ID,

PRCHG_DESC,

DEPT_NBR,

EVNT_CTG_CDE,

PRCHG_TYP_CDE,

PRCHG_ST_CDE,

from T_PRCHG

MINUS

select PRCHG_ID,

PRCHG_DESC,

DEPT_NBR,

EVNT_CTG_CDE,

PRCHG_TYP_CDE,

PRCHG_ST_CDE,

from PRCHG

Both the source and target table record values should return zero records


Pass Stev

3 Check for Insert strategy to load records into target table.

Identify a one record from the source which is not in target table. Then run the session

It should insert a record into target table with source data


Pass Stev

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Step

#


Actual Results,

Pass or Fail

(P or F)

Tested By

4 Check for Update strategy to load records into target table.

Identify a one Record from the source which is already present in the target table with different PRCHG_ST_CDE or PRCHG_TYP_CDE values Then run the session

It should update record into target table with source data for that existing record


Pass Stev

5 UNIX

How strong you are in UNIX?

1) I have Unix shell scripting knowledge whatever informatica required like

If we want to run workflows in Unix using PMCMD.

Below is the script to run workflow using Unix.

cd /pmar/informatica/pc/pmserver/

/pmar/informatica/pc/pmserver/pmcmd startworkflow -u $INFA_USER -p $INFA_PASSWD -s $INFA_SERVER:$INFA_PORT -f $INFA_FOLDER -wait $1 >> $LOG_PATH/$LOG_FILE

2) And if we suppose to process flat files using informatica but those files were exists in remote server then we have to write script to get ftp into informatica server before start process those files.

3) And also file watch mean that if indicator file available in the specified location then we need to start our informatica jobs otherwise will send email notification using

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Mail X command saying that previous jobs didn’t completed successfully something like that.

4) Using shell script update parameter file with session start time and end time.

This kind of scripting knowledge I do have. If any new UNIX requirement comes then I can Google and get the solution implement the same.

Basic Commands:

Cat file1 (cat is the command to create none zero byte file)cat file1 file2 > all -----it will combined (it will create file if it doesn’t exit)cat file1 >> file2---it will append to file 2

o > will redirect output from standard out (screen) to file or printer or whatever you like.

o >> Filename will append at the end of a file called filename.

o < will redirect input to a process or command.

How to create zero byte file?

Touch filename (touch is the command to create zero byte file)

how to find all processes that are running

ps -A

Crontab command. Crontab command is used to schedule jobs. You must have permission to run this command by Unix Administrator. Jobs are scheduled in five numbers, as follows.

Minutes (0-59) Hour (0-23) Day of month (1-31) month (1-12) Day of week (0-6) (0 is Sunday)

so for example you want to schedule a job which runs from script named backup jobs in /usr/local/bin directory on sunday (day 0) at 11.25 (22:25) on 15th of month. The entry in crontab file will be. * represents all values.

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25 22 15 * 0 /usr/local/bin/backup_jobs

The * here tells system to run this each month. Syntax is crontab file So a create a file with the scheduled jobs as above and then typecrontab filename .This will scheduled the jobs.

Below cmd gives total no of users logged in at this time.

who | wc -l

echo "are total number of people logged in at this time."

Below cmd will display only directories

$ ls -l | grep '^d'

Pipes:

The pipe symbol "|" is used to direct the output of one command to the input

of another.

Moving, renaming, and copying files:

Cp file1 file2 copy a file

mv file1 newname move or rename a file

mv file1 ~/AAA/ move file1 into sub-directory AAA in your home directory.

rm file1 [file2 ...] remove or delete a file

To display hidden files

ls –a

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Viewing and editing files:

cat filename Dump a file to the screen in ascii.

More file name to view the file content

head filename Show the first few lines of a file.

head -5 filename Show the first 5 lines of a file.

tail filename Show the last few lines of a file.

Tail -7 filename Show the last 7 lines of a file.

Searching for files :

find command

find -name aaa.txt Finds all the files named aaa.txt in the current directory or

any subdirectory tree.

find / -name vimrc Find all the files named 'vimrc' anywhere on the system.

find /usr/local/games -name "*xpilot*"

Find all files whose names contain the string 'xpilot' which

exist within the '/usr/local/games' directory tree.

You can find out what shell you are using by the command:

echo $SHELL

If file exists then send email with attachment.

if [[ -f $your_file ]]; thenuuencode $your_file $your_file|mailx -s "$your_file exists..." your_email_addressfi

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Below line is the first line of the script

#!/usr/bin/sh

Or

#!/bin/ksh

What does #! /bin/sh mean in a shell script?

It actually tells the script to which interpreter to refer. As you know, bash shell has some specific functions that other shell does not have and vice-versa. Same way is for perl, python and other languages.

It's to tell your shell what shell to you in executing the following statements in your shell script.

Interactive History

A feature of bash and tcsh (and sometimes others) you can use

the up-arrow keys to access your previous commands, edit

them, and re-execute them.

Basics of the vi editor

Opening a file

Vi filename

Creating text

Edit modes: These keys enter editing modes and type in the text

of your document.

i Insert before current cursor position

I Insert at beginning of current line

a Insert (append) after current cursor position of 112


A Append to end of line

r Replace 1 character

R Replace mode

<ESC> Terminate insertion or overwrite mode

Deletion of text

x Delete single character

dd Delete current line and put in buffer

:w Write the current file.

:w new.file Write the file to the name 'new.file'.

:w! existing.file Overwrite an existing file with the file currently being edited.

:wq Write the file and quit.

:q Quit.

:q! Quit with no changes.

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dwh material

Documents