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SELF LEARNING PROGRAM For DataStage Training

Self learning skills involve the ability to manage learning tasks without having them directed by others. They are skills necessary for effective lifelong learning and are one of many learning skills students are expected to develop by themselves.

GROUP COACHING PROGRAM For DataStage Training

Group coaching programs are cohort-based coaching with multiple clients enrolling and having a guided experience through the same program at the same time.

1 TO 1 COACHING PROGRAM For DataStage Training

One-on-one coaching is the process of one person helping another overcome an issue or improve performance. It revolves around sharing experiences and knowledge around specific topics or areas, showing how to best approach different situations and laying out the steps that should be used when executing the task.

About Us

We provide the best online classes to help you learn DataStage data integration, ETL, data warehousing. We also support onJob support for DataStage and IBM Information Analyzer tools.

About the trainer

Having 13+ Years experience in Data Analytics working on DataStage, IBM Information Analyzer and Pentaho Data Integration(PDI). Working as Trainer / On Job Support Consultant from Nov 2018 to till date.

DATA WAREHOUSE FUNDAMENTALS - DATASTAGE

  • An Introduction of Data warehousing
  • Purpose of Data warehouse
  • Data ware Architecture
  • OLTP vs. Data warehouse Applications
  • Data Marts
  • Data warehouse Lifecycle & SDLC
  • Introduction of Data Modeling
  • Dimensions and Fact Tables
  • Logical Modeling
  • Physical Modeling
  • Schemas like Star Schema & Snowflake Schemas
  • Fact less Fact Tables Content

DataStage Training Syllabus

  • DataStage Introduction
  • DataStage Architecture
  • DataStage Clients
  • Designer
  • Director
  • Administrator
  • DataStage Workflow

DataStage Training Syllabus

  • Parallel Jobs, Server Jobs & Job Sequences
  • Setting up DataStage Environment
  • DataStage Administrator Properties
  • Defining Environment Variables, run time parameter and parameter sets
  • Importing Table Definitions

Accessing DataStage File / Xml Data

  • Sequential File stage
  • Data Set stage
  • Complex Flat File stage
  • Fileset stage
  • Hash file
  • Cloud data files
  • Create jobs that read from and write to sequential files
  • Read from multiple files using file patterns
  • Use multiple readers
  • Null handling in Sequential File Stage
  • External Source & Target stages
  • Could connectivity file stages
  • Working with Fixed and Delimiter formats
  • Other Misc stages

DataStage Platform Architecture

Describe parallel processing architecture Describe pipeline & partition parallelism

List and describe partitioning and collecting algorithms

Describe configuration files & usages.

DataStage
Combining Data

Combine data using the Lookup stage

Combine data using merge stage

Combine data using the Join stage

Combine data using the Funnel stage

Best practice methodology in data merging concepts.

DataSatge Sorting, Aggregating Data &
Data Filter

Sort data using in stage sorts and Sort stage

Combine data using Aggregator stage

Remove Duplicates stage

Data filter using Switch, Filter and External Filter stages

Removing duplicate using Link Partition technique

DataStage Transformer Methods & All the new features

  • Understand ways DataStage allows you to transform data
  • Create column derivations using user defined code and system functions
  • Filter records based on business criteria & Control data flow based on data conditions
  • String function
  • Datatype conversion
  • Stage Variables
  • System Variables
  • Loop Variables
  • Transformation functioned

DataStage Repository Functions

  • Perform a simple Find & perform an Advanced Find Perform an impact analysis
  • Compare the differences between two Table Definitions and Jobs
  • Predefined rules and usages
  • Parameter sets storage

DataStage Training - Advanced Processing Stages

  • Pivot Enterprise stage
  • Surrogate key stage
  • Looping variables
  • Real time scenarios using different Processing Stages
  • Implementing different logics using Transformer
  • Real time Stages
  • Restructure stage
  • Overview of Data Quality Stages

DataStage Training - Advanced Processing Stages

DataStage advance processing stages

  • Pivot Enterprise stage
  • Surrogate key stage
  • Looping variables
  • Real time scenarios using different Processing Stages
  • Implementing different logics using Transformer
  • Real time Stages
  • Restructure stage
  • Overview of Data Quality Stages

DataStage working with Relational Databases

  • Import Table Definitions for relational tables
  • Create Data Connections - ODBC and RDMS stages
  • Use Connector stages in a job
  • Respective Database Connectors Stage
  • Connectors with Cloud database like AWS, Snowflake, BigQuery, Azure

DataStage Development & Debugging stages

  • Head
  • Tail
  • Peek
  • Column generator
  • Row generator
  • Sample stage
  • Range stage

Working with Data Capture / SCD types Concepts

  • Working with Compare difference and checksum stages.
  • Data Capture and update using change data capture, change apply
  • SCD steps implementations

Metadata in Parallel Framework

  • Create schemas & Details
  • Explain Runtime Column Propagation (RCP).
  • Build a job that reads data from a sequential file using a schema.
  • Build a local & shared container.
  • Importing table definitions
  • Manually entry process for metadata

DataStage Training - Job Sequence Contro

DataStage Job Sequence Control

  • Use the DataStage Job Sequencer to build a job that controls a sequence of jobs.
  • Use Sequencer links and stages to control the sequence a set of jobs run in.
  • Use Sequencer triggers and stages to control the conditions under which jobs run.
  • Pass information in job parameters from the master controlling job to the controlled jobs.
  • Parallel mode job sequence creation
  • Job Sequences creation in Sequence mode
  • Define user variables & Routines usage
  • Enable restart.
  • Handle errors and exceptions
  • Dynamics run time parameter passing.
  • Batch job creation.
  • Scheduling jobs in DataStage Director.
  • Benefits of exception handler.
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