• Connector stage overview
• - Use Connector stages to read from and write to relational tables
• - Working with the Connector stage properties
• Connector stage functionality
• - Before / After SQL
• - Sparse lookups
• - Optimize insert/update performance
• Error handling in Connector stages
• - Reject links
• - Reject conditions
• Multiple input links
• - Designing jobs using Connector stages with multiple input links
• - Ordering records across multiple input links
• 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

• 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

• 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

• 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
• Use the Data Rules stage to valid foreign key references in source data
• Create custom data rules
• Demonstration 1: Using data rules

• Introduction to the Hierarchical stage
• - Hierarchical stage Assembly editor
• - Use the Schema Library Manager to import and manage XML schemas
• Composing XML data
• - Using the HJoin step to create parent-child relationships between input lists
• - Using the Composer step
• Writing Hierarchical data to a relational table
• Using the Regroup step
• 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

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

Additional course details:

Nexus Humans KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.