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2M423G - IBM InfoSphere DataStage v11.5 - Advanced Data Processing

Overview

Duration: 2 days

This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.

Objectives

  • Use Connector stages to read from and write to database tables

  • Use the File Connector stage to read from and write to Hadoop HDFS files

  • Handle SQL errors in Connector stages

  • Use the Unstructured Data stage to extract data from Excel spreadsheets

  • Use the Big Data stage to read from and write to Hadoop HDFS files

  • Use the Data Masking stage to mask sensitive data processed within a DataStage job

  • Use the XML stage to parse, compose, and transform XML data

  • Use the Schema Library Manager to import and manage XML schemas

  • Use the Data Rules stage to validate fields of data within a DataStage job

  • Create custom data rules for validating data

  • Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions

  • Use the Surrogate Key Generator stage to generate surrogate keys

Content

Unit 1 –Accessing databases

Topic 1:  Connector stage overview

  • Use Connector stages to read from and write to relational tables
  • Working with the Connector stage properties

Topic 2:  Connector stage functionality

  • Before / After SQL
  • Sparse lookups
  • Optimize insert/update performance

Topic 3:  Error handling in Connector stages

  • Reject links
  • Reject conditions

Topic 4:  Multiple input links

  • Designing jobs using Connector stages with multiple input links
  • Ordering records across multiple input links

Topic 5:  File Connector stage

  • Read and write data to Hadoop file systems

Demonstration 1: Handling database errors

Demonstration 2:  Parallel jobs with multiple Connector input links

Demonstration 3:  Using the File Connector stage to read and write HDFS files

 

Unit 2 – Processing unstructured data

Topic 1:  Using the Unstructured Data stage in DataStage jobs

  • Extract data from an Excel spreadsheet
  • Specify a data range for data extraction in an Unstructured Data stage
  • Specify document properties for data extraction.

Demonstration 1:  Processing unstructured data

 

Unit 3 – Data masking

Topic 1:  Using the Data Masking stage in DataStage jobs

  • Data masking techniques
  • Data masking policies
  • Applying policies for masquerading context-aware data types
  • Applying policies for masquerading generic data types
  • Repeatable replacement
  • Using reference tables
  • Creating custom reference tables

Demonstration 1: Data masking

 

Unit 4 – Using data rules

Topic 1:  Introduction to data rules

  • Using the Data Rules Editor
  • Selecting data rules
  • Binding data rule variables
  • Output link constraints
  • Adding statistics and attributes to the output information

Topic 2:  Use the Data Rules stage to valid foreign key references in source data

Topic 3:  Create custom data rules

Demonstration 1:  Using data rules

 

Unit 5 – Processing XML data

Topic 1:  Introduction to the Hierarchical stage

  • Hierarchical stage Assembly editor
  • Use the Schema Library Manager to import and manage XML schemas

Topic 2:  Composing XML data

  • Using the HJoin step to create parent-child relationships between input lists
  • Using the Composer step

Topic 3:  Writing Hierarchical data to a relational table

Topic 4:  Using the Regroup step

Topic 5:  Consuming XML data

  • Using the XML Parser step
  • Propagating columns

Topic 6:  Transforming XML data

  • Using the Aggregate step
  • Using the Sort step
  • Using the Switch step
  • Using the H-Pivot step

Demonstration 1:  Importing XML schemas

Demonstration 2: Compose hierarchical data

Demonstration 3: Consume hierarchical data

Demonstration 4:  Transform hierarchical data

 

Unit 6:  Updating a star schema database

Topic 1:  Surrogate keys

  • Design a job that creates and updates a surrogate key source key file from a dimension table

Topic 2:  Slowly Changing Dimensions (SCD) stage

  • Star schema databases
  • SCD stage Fast Path pages
  • Specifying purpose codes
  • Dimension update specification
  • Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions

Demonstration 1: Build a parallel job that updates a star schema database with two dimensions 

Audience

Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources.

Prerequisites

DataStage Essentials course or equivalent.

Certification

Schedule

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