This course explains some advanced topics for the DataFlux technology through a combination of hands-on exercises, demonstrations, and classroom discussions.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

This course can help prepare you for the following certification exam(s): SAS Data Quality using DataFlux Data Management Studio.

Learn how to develop advanced skills and techniques of working with DataFlux Data Management Studio, including

• Working with the DataFlux Expression Engine Language (EEL).
• Creating custom metrics.
• Using macro variables.
• Creating process jobs.

Who should attend

Data quality stewards

Formats available	Standard Duration
Classroom:	3 days
Live Web Classroom:	6 half-day session(s)	System Requirements

Before attending this course, you should attend DataFlux® Data Management Studio: Essentials or have equivalent knowledge.

This course addresses DataFlux Data Management Studio, DataFlux Data Management Server software.

Introduction

• Overview of course topics.
• Introduction to course data.

DataFlux Expression Engine Language

• Introduction to DataFlux Expression Engine Language (EEL).
• Data profiling using Expression Engine Language (EEL).

Expression Node in Data Jobs

• Working with the Expression node.
• Working with the IF-ELSE statement.
• Working with the RETURN statement.
• Using the PUSHROW function.
• Using the grouping functionality.

Custom Metrics

• Reviewing custom metrics.
• Working with custom metrics in a data profile.
• Working with custom metrics in business rules.
• Working with custom metrics in a data job.

Parameterization with Macros

• Creating a macro file.
• Using a macro variable in a data profile.
• Using macro variables in a data job.
• Using macro variables in data monitoring.

Essentials of Process Jobs

• Introducing process jobs.
• Creating a process job executing two data jobs.
• Examining source bindings in a process jobs.

Creating Advanced Process Jobs

• Investigating conditional processing.
• Working with work tables and events.
• Investigating parallel processing using events and the Event Listen node.
• Investigating parallel processing using the Fork node.

Tips, Tricks, and Other Topics

li>Discuss how data is processed in a data job.
• Discuss job optimization.
• Discuss various tips for building and testing jobs.
• Discuss a command for deployed jobs.
• Discuss topics for promoting to production.