Datastage trining
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This document provides an overview of IBM WebSphere DataStage and QualityStage version 8.0.1. It discusses key concepts in data warehousing and ETL using DataStage such as dimensional modeling, fact tables, slowly changing dimensions, and DataStage job components. It also compares server jobs to parallel jobs, and partitioning techniques. Finally, it introduces QualityStage and its data quality stages for standardizing, matching, and deduplicating data.
Datastage trining
- 1. Ramachandra Technologies.Pvt.Ltd Datastage (Parallel jobs & server jobs) DATAWAREHOUSING CONCEPTS Introduction to Data Warehousing What is Data Warehousing? Types of Systems OLTP,OLAP Data Warehousing Life Cycle Data Warehousing Architecture Source, Integration Layer, Staging Area Target Analysis & Reporting ODS Dimensional Modeling What is dimension modeling? Difference between ER modeling and dimension modeling What is a Dimension? What is a Fact? Start Schema Snow Flake Schema Difference between Star and snow flake schema Fact Table, Different types of facts Fact less Fact Table Dimensional Tables Confirmed Dimensions, Unconfirmed Dimensions, Junk Dimensions, Degenerative Dimensions What are slowly changing Dimensions? Different types of SCD’s IBM WEBSPHERE DATA STAGE AND QUALITY STAGE VERSION 8.0.1 Contents Introduction about Data Stage Difference between Data Stage 7.5.2 and 8.0.1 What’s new in Data Stage 8.1? What is way ahead in Data Stage? IBM Information Sever architecture Datastage within the IBM Information Server architecture Difference between Server Jobs and Parallel Jobs Difference between Pipeline Parallelism and Partition Parallelism Partition techniques (Round Robin, Random, Hash, Entire, Same, Modules, Range, DB2, Auto) Configuration File Difference between SMP/PMP(Cluster) Architecture Data stage components (Server components /Client components) Designer Introduction about Designer Repository, Palette Type of Links File Stages Sequential file Dataset file File set Lookup file set Difference between Sequential file/Dataset/File set Overview of iWay, Classic federation and netezza Database Stages Dynamic RDBMS
- 2. Ramachandra Technologies.Pvt.Ltd Oracle Enterprise ODBC Enterprise Stored Procedure Processing Stages Change Capture Compare Stage Difference Stage Aggregate Stage Transformer Stage Difference between basic transformer and transformer Surrogate Generator Stage Join Stage Merge Stage Lookup Stage Difference between Join/Lookup/Merge Difference between Join/Lookup Remove Duplicates Switch Pivot Modify Funnel Generic stage Different types of sorting and sort stage. Different types of combining and collecting techniques. Filter, External filter Difference between filter, External filter and switch stages. SCD stage Encode and decode stages FTP stage Adding job parameters to a job, Parameter set Difference between partitioning and re partitioning Run time column propagation Schema files Debugging Stage Head, Tail, PeaK Row Generator Column Generator Sample Containers Difference between Local Container and Shared Container Local Container Shared Container Job Sequencers Arrange job activities in sequencer Triggers in Sequencer Notification activity, Terminator Activity, Wait for file activity Start loop activity , Execute command activity Nested Condition activity, Routine activity Exception handing activity, User variable activity, End loop activity Adding Checkpoints Data stage Director Introduction to Data stage Director Job Status View, View logs, Scheduling Batches Creation Cleaning resources using Administrator Web sphere Manager in Designer Introduction about Data stage Manager Importing the Job, Exporting the Job
- 3. Ramachandra Technologies.Pvt.Ltd Importing Table Definition Different types of table definitions and their differences. Importing Flat File Definition Routines Dataset management and ORCHADMIN Quick search and Advanced search Data stage Administrator Creating project, Editing project and Deleting project Permissions to user Different kinds of variables in Data Stage Cleaning resources using Administrator Web sphere Quality Stage What is Date Quality and why do we for data quality? Integration of Data Quality in Data Stage? Data stage Quality stages Investigate stage, Standardize stage Match Frequency stage Unduplicate Match stage, Reference Match stage, Survive stage Standardized rule sets. Components of Standardized rule sets.