Introduction

Introduction - Oracle Data Integration 

Data Integration:

“Data integration is the combination of technical and business process used to combine data from disparate sources into meaningful and valuable information”.

Data integration ensures that information is timely, accurate and consistent across complex systems.

Types of Integration:

1. Data Integration (large volume of data Integration)
2. Event Integration (Event Drive Architecture)
3. Service Integration ( Service Oriented Architecture)

ODI features:

• Oracle Data Integration features an active integration platform that includes all styles of data integration : data-based, event-based and service-based.
•  Capable of transforming large volume of data efficiently
• Processing of events in real time through its advanced Change Data Capture (CDC) Capability
• Providing data services to Oracle SOA suite

 E-LT Architecture:

Traditional ETL:

ETL tools Operate by first extracting the data from various sources, transforming the data to proprietary or middle-tier ETL engine , and then loading the transformed data onto the target data warehouse or integration server.

Here data must be moved over the network twice:

• Once between source and ETL server
• Again Between ETL server and target data Warehouse

ETL engine performs data transformation row-by-row basis so it will be more difficult in some situations. Moreover, if you want to ensure the referential integrity by comparing data flow references against the value from target data warehouse, the referenced data must be downloaded from target to the engine, thus further increase the traffic in the network.

E-LT

Extract data from sources, load to tables into destination server, and then transform data on the target RDBMS using native SQL operators.

Data
        Meaningful information is nothing but "Data".
Types:

1. Analytical Data (Data WareHouse)
2. Transactional Data

Transactional Data 

1. Its run time data or day to day data
2. It’s current and detail
3. It’s useful to run Business
4. It is used to store in OLTP (On Line transaction Processing)
5. Source of Transactional data is Application
6. Example ATM Transactions , Share market Transaction etc

Analytical Data

1. It is useful analyze to the business
2. It is historical and Summarized data
3. It is stored in OLAP or Data Warehouse
4. Source of Analytical Data is OLTP