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

Description

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. http://www.ibm.com/training/terms

Unit 1 Accessing databases
Topic 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 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 Processing unstructured data

Unit 3 Data masking
Topic 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 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 Using data rules

Unit 5 Processing XML data
Topic 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 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 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

Aim

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

Participant requirements

DataStage Essentials course or equivalent.

Participant

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