informatica for sap

Informatica for SAPInformatica Solutions for SAP Applications

Presented by Stefan Manns, Informatica

SAP Data Integration Needs of Organizations

SAP applications need to be

migrated and modernized

SAP data is required for

strategic and tactical

decisions

SAP data may not meet Data

Quality expectations

Sensitive SAP data should be protected

from breaches

Why is Data Integration for SAP difficult?

Data has proprietary format and structure

Steep learning curve for

developers

SAP programmers

are not a resource for

data integration

Possible “Solutions”

Attempt to hand code a “solution”

Point solutions for

small, departmental

problems

Create organizational problems by

relying on SAP programmers

Manual “process”

using Excel etc.

Data Matching

Point of EntryDQ for SAP

Data Profiling

Data Cleansing

Address Validation

SAP Masking

BW Nearline

SAP Archive SAP Retirement

SAP SubsetTable read

ABAP

BAPI

BCI (contentExtractor)

Table writerIDOC

DMI(LSMW)

SAP BW(OHD/OHS)

AccessHANA

Platform Capabilities: SAP Focus

Informatica Solutions for SAP

Connectivity• Batch access• Real-time access• CDC access

Integration Patterns• Operational and analytical data integration• Data quality• Virtualization• Cloud based integration

Information Lifecycle Management• Manage Test Data• Protect Sensitive Data• Improve application performance

Connectivity1

HANA

Row table

In memory

Columntable

Informatica

Extract HANA

Cleanse,Join,

Transform

Any data

Load HANA

*) requires SAP HANA

client libraries

Extract and Load SAP HANA

ECC

Cluster tables

Transparenttables

Pool tables

Informatica

Receive table data

Cleanse,Join,

TransformData from

SAP

Operational EDW App

Auto generate

ABAP program

Table Read (Auto Generated ABAP)

ECC

e.g. Salesorder

create

BAPI interfaces

e.gCustomer

getlist

Informatica

Receive response

Cleanse,Join,

TransformData from

SAP

Operational EDW App

InvokeBAPI

BAPI (Business API)

ECC

e.g.Orders05

IDOC

e.g.Debmas06

InformaticaInbound

IDOC

Cleanse,Join,

TransformData from

SAP

Operational EDW App

OutboundIDOC

IDOC (Intermediate Document)

ECC

BDC

Batch Input

LSMW

InformaticaCreate

properly structured

file for LSMW or BDC

LegacyData

DMI (Data Migration Interface)

ECC

DataSourceDelta

DataSource

DataSourceFull

SAP Data viaBus Content

BI (BW)DataSource

PSA

InfoSource ODS Object

DataTarget

InfoCube

InfoProvider

Mapping &Transfer

Rules

UpdateRules

UpdateRules

InformaticaSAP Data viaBus Content

Cleanse,Join,

TransformData from

SAP

Operational EDW App

BCI (Business Content Integration)

BI (BW)DataSource

PSA

InfoSource DSO

DataTarget

InfoCube

InfoProvider

Mapping &Transfer

Rules

UpdateRules

UpdateRules

Informatica

BW extraction

via Open Hub

Destination

Any data

BW load to

PSA via

DataSource

Extract and Load SAP BW

Summary and Properties of Methods

Integration technique

Direction Volume Change capture

Use case

HANA RW Low to high n/a Both

ABAP R High Only by date stamp

Analytical

BAPI RW Medium no Operational

IDOC RW Low/medium Only if SAP change pointer

Operational

DMI W High n/a Migrations

BCI R High Yes Both

Integration Patterns2

Integration Patterns with SAP

Analytical Data Integration

Operational Data Integration

CloudData Integration

Data Quality with SAP


• Build a Data Warehouse using SAP sources


Enterprise Class Performance & Support

High Availability

Pushdown Optimization

Proactive Data Integration Monitoring

Grid Computing

Real-time DW

Data Marts

Big Data

Data Warehouse

Enterprise DW

Table readABAP


SAP BW(OHD/OHS)

AccessHANA

HANA

Building a Data Warehouse

Actual Customers Building a Data Warehouse

A large hardware cooperative:• EDW for financial close processing• 60 MM rows/day with BCI• Meeting SLA for first time in 2 years

A regulated electrical utility:• EDW combining smart meter data

with customer detail from SAP• Using ABAP method and Open Hub• Customer likes ease of use, ability to

get data quickly and flexibility for prototyping

• Combine non SAP and SAP data through Virtualization



Virtualized Data Access

PortalBI Composite Apps

Enterprise Data Sources

Data Abstraction

Logical Data Objects

CUSTOMER ORDERPRODUCT INVOICE

DataConsumers

Informatica Data Services

• Define physical source

• Create Logical Data Object

• Provide virtual schemas/tables/views

• Data Consumers access virtualization via ODBC/JDBC

Logical View of Underlying Resources

and any nonSAP data


• Integrate daily General Ledger

• Migrate into SAP

• Enable MDM

Operational Data Integration

Integrate Daily General Ledger

IDOC

Migrate into SAP

25

Extraction

Harmonization

Conversion

Iterative Load

Migration

Validation

BAPITable writer

IDOCDMI

(LSMW)

Enable MDM

26

Use extractorsor “Change Pointers”

IDOC


Actual Customer Using Operational DI

A film and television studio:• SAP is central to business – one

finance, one supply chain instance• Informatica is operational DI layer• Integrations around purchase orders,

GL postings, supply chain


• Migration from SAP to SaaS

• Analysis

• Synchronization of SAP data to downstream systems

Cloud Data Integration

Example: Synchronize Data

29

Example: Synchronize Data

Customers Leveraging Cloud Services

• Complete integration projects in weeks not months

• Reduce Data Integration Time by nearly 85 Percent

• Achieve real business impact in terms of revenue and profit


• Analyze SAP data

• Validate, match and standardize all data types

• Ensure Data Quality at point of entry

Data Quality with SAP

Directly Profile SAP Data andGet to Know your Data

Value FrequencyPatternsStatistics

Unique Value Count

Null Value CountInferred Datatype

Documented Datatype

Minimum / Maximum

• Country specific address cleansing

• Contact data cleansing (phone, email, gender, SSN etc. for many countries)

• Company/corporate level data cleansing

• Matching for many entities and countries

Rich library of out of the box DQ rules

34

• Validate whether the given address is correct using reference address data

35

Point of Entry – Real Time Address Validation

• Retrieve records similar to a given name and address to avoid entering duplicates into the system

36

Point of Entry – Real Time Duplicate Check

• Perform a search using incomplete or partially incorrect information

37

Point of Entry – Fuzzy Search

Solution Benefits

Native bi-directional access to SAP data

Rapid development for all types of DI

Easily and Quickly Leverage specialized SAP

methods

Allow business user to

build SAP integrations

Integrate SAP as on

demand service

Easily and Quickly identify Anomalies with SAP Data

Eliminate or fix incorrect data

Reduce duplication and achieve single version of

truth

DI Benefits Cloud Benefits DQ Benefits

Information Lifecycle Management

3

Manage Test Data in SAP Environments

Source System

Subset forSAP

Target System

SAP target systemSAP source system

Moves master and transactional data

1 TB

1 TB

1 TB

1 TB

PRD6 TB

Dev6 TB

QA6 TB

Train6 TB

Benefits of Test Data Management

41

Data Subset

Time Savings

Here

Space SavingsHere

Protect Sensitive Data in SAP Environments

Source System

Masking forSAP

Target System

SAP target systemSAP source system

Inline data masking

Source System

Masking forSAP

SAP source system mask in place

Permanently Mask Sensitive Data

Permanently alter sensitive data such as credit cards, address information, or names

ID Name City Credit Card

Tampa

Hartford

Modesto

Plano

Fresno

Fresno

Fresno

Fresno0964

9388

2586

7310 Jeff Richards

Rob Davis

Mark Jones

John Smith

Josh Phillips

Andy Sanders

Jerry Morrow

Mike Wilson

4198 9148 1499 1341

4298 0149 0134 0148

4981 4078 9149 1491

4417 1234 5678 9112

4198 9481 9147 0521

4298 9341 9544 9114

4981 1341 0854 0508

4417 9741 1949 9471• Shuffle Employee ID’s• Substitute Names• Constant for City• Special Credit Card Technique

Variety of Techniques:

Dynamically Mask Sensitive Data

Role-based anonymization and real-time prevention while maintaining

operational efficiency across environments

Values Presented:

BL****

JO****

KI****

View for authorized user

Database

Private Information Stored in Database

BLAKE

JONES

KING

Values Presented:

BLAKE

JONES

KING Select nam

e from table1

Dynamic Data Masking Layer applies real-time SQL rewrites

to mask returned result set

Data is masked for the selected group of users. Masks even

for SAP GUI

(1) S

elec

t nam

e fr

om ta

ble1

(2)Select substring(name,1,2)||’***’ from table1

Customers Masking Sensitive Data

• Comply with regulations such as HIPAA, PCI DSS, GLBA and many more

• Control access for 100s of developers

• Mask personally identifiable recruiting and HR data

Features

Benefits• Reduced TCO Reduction of hardware costs for

SAP BW (online database, BWA, and HANA).

• Increased data accessibility More historical data available for ad-hoc analyses

• Optimized investments based on what is important to the business Performance over storage

• Lower administration, ability to meet SLAs Smaller more manageable online database which provides faster querying and data loads

• First SAP certified solution

• Stores infrequently used data relieving online system

• Efficient storage of nearline data Up to 98% compression, average 90%+.

• Fast seamless access to nearline data Via standard SAP BW access methods

• 100% Indexed with no index maintenance

• Ensures immutability

Increase SAP BW Performance

Customers Using a Nearline Strategy

• Move 100s GB of data to Nearline• Achieve very high compression

rate (> 95%)• In one case saving $840K in 18

months

Integrate Archiving with 3rd Party Storage

SAP ADK / Un-

structured File

SAP ADK / Un-

structured File

SAP Archive ProcessSAP

Master Data

ScannedImages

ScannedImages

Scanning Process

ARCHIVEBRIDGE

3rd Party / External Archive Store

SAP certified ArchiveLink implementation

Store and retrieve SAP archive files, scanned images, print lists, incoming and outgoing

documents, etc.

Access archive data via SAPGUI as usual

Retire Obsolete SAP Instances

ConnectRetireVerify

OptimizedFile Archive

PurgedData

AccessManagePurge

• Specific SAP support (e.g. pooled and clustered tables)

• Validate completeness of data before decommissioning

• Store retired data in a massively compressed and accessible file archive

• Manage retention policies, access data, and purge data permanently

• Achieve compliance with all government regulations and industry standards

• Bi-directional, rapid development access to SAP data

• Discover and remedy data quality challenges

• Achieve single version of truth for all data types

• Leverage ease of use and speed of Cloud Integration

• Quickly create lean, purpose built test data subsets

• Control risk of data security breaches

• Achieve significant cost savings for BW environments

• Retire obsolete SAP instances with regulatory compliance

Informatica Solutions for SAP: Benefits

Key Sessions To Attend with Hands on Labs

Solutions for Application Owners Improve Performance

PP5562 Informatica Solutions for Oracle

HIT5541 Cloud ILM

BP 7025: Cox Communications Improved sales Performance with Informatica

PP5199: What’s New from INFA to improve DW Performance

BD4641: Big Data and Its Impact on Performance

Data Privacy Test Data Management

HIT5541 Cloud ILM

TT6659 Deploying Test Data Services and Dynamic Data Masking with HOT CISO

BP7043 Accelerating Business Growth, Protecting Sensitive Client Financial Data

PP5562 TDM and Data Privacy for Apps/ DW

BP4966 Effective Test data management practice in complex distributed system

Partner 7020 Cognizant

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