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Architecture of Data Stage

 

Repository :- / Work Area / Project

It is a work Area with collection of different Components.

  • Different Repository Components are
  • Table Definition
  • Shared Contains
  • Routines ..etc
  • Jobs

 

It is an Environment for developing ETL jobs using the components.

Engine :- /Data stage Server

  • Responsible for Executing Data stage jobs.
  • Engine is also Responsible for partitioning, Re- partitioning and Reverse partitioning.

Plugins are used for communication with external sources or target

Screenshot_35

 

  • Though interface, the local of oracle are added to the Data stage
  • Plug – ins will reside in package Installer

Packs

We need to install Data stage packages using packes of package Installer

Client

Components

  • Designer
  • Director
  • Manager
  • Admin

 

Server

  • Repository
  • Engine
  • Package Installer
  •  client
  • Designer
  • Director
  • Manager
  • Admin

 

8.0.1:

Common user interface :- / unified users:

  • Designer
  • Director
  • Web console
  • Admin
  • Information Analyzer

 

 

  1. Common repository : / Meta Data Server

Common Environment  to store w. r. t. Data Quality Data profiling and data Transformation.

  1. Common Engine :-

Responsible for information Analysis , Data Quality and data Transformation.

  1. Common Connectivity:-

Responsible for plug ins  and packs.

  1. Common Shared Services :–

Acts as interface between all components

Screenshot_36

 Architecture

         2 – layer Architecture

Screenshot_11

 

 

– – >  W – H Inman is known as the faster of warehouse .

– – > Top – bottom approach was given by W – H Inman.

– – >Bottom – up approach was given by Ralph – Kimball.

 

Screenshot_12

Aspired to become an DataStage?
Explore the post to discover the know-hows on DataStage Training.

L4  Project Architecture

Screenshot_13

 

Different format of .txt

– – >  Fixed width flat files

– – >   Comma separated values

– – >  Variable length files

– – >   Head and tail files

 

Screenshot_14

For indepth understanding of DataStage click on

 

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Architecture of Data Stage
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