This course introduces DataFlux Data Management Studio and includes topics for data profiling, data jobs to perform data management tasks (such as data quality and entity resolution), data monitoring, usage of DataFlux Expression Engine Language, macro variables, and process jobs.

• Understand data explorations.
• Create and review data profiles.
• Create data jobs to improve data quality.
• Create data jobs to perform entity resolution.
• Establish monitoring aspects for your data.
• Work with the DataFlux Expression Engine Language.
• Define and use macro variables.
• Create process jobs.

Formats available	Standard duration
Live Web Classroom:	8 half-day session(s)
e-Learning:	28 hours/180 day license

There are no prerequisites for this course.

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

• Introduction to DataFlux Data Management offerings and architecture.
• Methodology and course flow.

• Navigating the Data Management Studio Interface.
• Verifying quality knowledge base and reference sources.
• Working with data connections.
• Creating a DataFlux repository.

• Creating and exploring data profiles.
• Profiling a subset of data.
• Profiling data in text files.

• Setting DataFlux Data Management Studio options.
• Creating, documenting, and running a simple data job.

• Performing a simple exploration of the QKB.
• Investigating standardization using standardization definitions and standardization schemes.
• Working with a Field Layout node.
• Working with parsing and casing.
• Investigating right fielding and identification analysis.

• Creating match codes.
• Clustering records.
• Adding survivorship to the entity resolution job.
• Adding field-level rules for the surviving record.

• Defining business rules.
• Data profiling with business rules and alerts.
• Working with data jobs and business rules.
• Creating and executing a task.

• Introduction and overview of DataFlux Expression Engine Language (EEL).
• Creating dynamic fields for a profile using EEL.

• Working with the Expression node.
• Reviewing the IF/ELSE statement.
• Reviewing return status.

• Creating a macro file.
• Using macros in a data profile.
• Using macros in a data job.

• Introduction to process jobs.
• Examining source bindings in a simple process job.

• Working with conditional processing.
• Working with work tables and events.

• Examining how data is processed in a data job.
• Considering job optimization techniques.
• Exploring tips for building and testing jobs.
• Working with the Data Management Server.
• Examining steps for promotion to production.