Data mart

Topics:

Dependent Data Mart and Operational Data Store Architecture

Logical Data Mart and @ctive Data Warehouse Architecture

Three-Layer Data Architecture

Some Characteristics of Data Warehouse Data

Dependent Data Mart and Operational Data Store Architecture

1. A separate ETL proceses is developed for each data mart, which can yield costly redundant data and processing efforts.

2. Data marts may not be consistent with one another, . . . .

3. There is no capability to drill down into greater detail or into related facts in other data marts or a shared data repository, so analysis is limited, or at best, very difficult.

4. Scaling costs are excessive because every new application, which creates a separate data mart, repeats all the extract and load ateps.

5. If there is an attempt to make the separate data marts consistent, the cost to do is quite high.

Dependent Data Mart

- A data mart filled exclusively from the enterprise data warehouse and its recociled data.

Two significant benefits of independent data mart approach

1. It allows for the concept of a data warehouse to be proved by working on a series of small projects.

2. The length of time unyil there is some benefit from data warehousing is reduced because the organization is not delayed until all data are centralized.

Logical Data Mart and @ctive Data Warehouse Architecture

Logical Data Mart

- A data mart created by a relational view of a data warehouse.

Unique Characteristics of Logical Data Mart

1. Logical Data Mart are not physically separate database

2. Data are moved into the data warehouserather than to a separate staging area.

3. New data marts can be created quickly becuase no physical database or database technology needs to be created or acquired and no loading routines need to be written.

4. Data marts are always up-to-date because data in a view are created when teh view is refernced; . . . . .

@ctive Data Warehouse Architecture

An enterprise data warehouse thata ccepts near-real-time feeds of transactional data from the systems of record, analyzes warehouse data, and in near-real-time relays business rules to tje data warehouse and systems of record so that immediate actin can be taken in response to business events.

Some beneficial application for @ctive Data Warehouse Architecture include:

Just-in-time transportation for reporting deliveries basedon up-to-date inventory levels

E-commerce where, for instance, an abandoned shoping cart can trigger an email promoyional message before the user signs off

Fraud detections in credit card transcations, where an unusual pattern of transactions coulod alert a sales clerk or online shopping cart routine to take extra precautions.

Three-Layer Data Architecture (Figure 11-6)

Reconciled data

-Detailed, current data intended to be the single, authorative source for all the decision support applications.

Three types of metadata as shown in the Figure 11-6

1. Operational Metadata

2. Enterprise data warehouse (EDW) Metadata

3. Data mart Metadata

Some Characteristics of Data Warehouse Data

Status versus Event data (Figure 11-7)

Event

A database action (create, update, or delete) that results from a transaction.

Transient versus Periodic Data

Transient data

- Data in which changes to existing recoerds are written over previous records, thus destroying the orevious data content.

Peridic data

- Data that are never physically altered or deleted once they have been added to the store.

Transient operational data(Figure 11-8)

Periodic warehouse data (Figure 11-9)

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Table X (10/05)

Key Date A B Action

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