In Hyderabad

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Important information

Typology Course
Start Hyderabad
Class hours 40 hours of class
  • Course
  • Hyderabad
  • 40 hours of class

Important information

Where and when

Starts Location
On request
516,Annapurna Block, AdityaEnclave, Ameerpet, Hyderabad-500026, 500026, Andhra Pradesh, India
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Starts On request
516,Annapurna Block, AdityaEnclave, Ameerpet, Hyderabad-500026, 500026, Andhra Pradesh, India
See map

Course programme

DATAWAREHOUSING CONCEPTS Introduction to Data Warehousing What is Data Warehousing? Who needs Data Warehousing? Why Data Warehouse is required? Types of Systems OLTP OLAP Maintenance of Data Warehouse Data Warehousing Life Cycle Data Warehousing Architecture Source Integration Layer Staging Area Target Analysis & Reporting ODS Multi-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 Dimensional Tables Fact less Fact Table Confirmed Dimensions Unconfirmed Dimensions Junk Dimensions Monster Dimensions Degenerative Dimensions What are slowly changing Dimensions? Different types of SCD’s IBMWEBSPHERE 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 8.0.1? 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 omponents (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 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 Pea 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 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. Match designer WAVES

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