There is a new version of this course. Please see DataFlux® Data Management Studio: Essentials.
This course is for data quality stewards who perform data management tasks, such as data quality improvements, data enrichment, and entity resolution.
The self-study e-learning includes:
Lernen Sie, wie Sie / Learn how to
- Annotatable course notes in PDF format.
- Virtual Lab time to practice.
- create and review data explorations
- create and review data profiles
- create data jobs for data improvement
- establish monitoring aspects for your data.
Zielgruppe / Who should attend
Data quality stewards
Verfügbare Schulungsformen / Formats available
There are no prerequisites for this course.
In diesem Kurs wird mit folgenden Software Modulen gearbeitet: DataFlux Data Management Studio Software
This course addresses DataFlux Data Management Studio 2.6 and DataFlux Data Management Server 2.6.
Introduction and Course Flow
DataFlux Data Management Studio: Getting Started
- providing an overview of the technology offerings for SAS Data Quality
- discussing the DataFlux Data Management Platform architecture
- navigating the DataFlux Data Management Studio interface
- creating a Data Managment Studio repository
- verifying the course QKB and reference sources
- working with data connections
ACT: Introduction to Data Jobs
- creating data collections
- designing data explorations
- creating data profiles
- designing data standardization schemes
- introduction to data jobs
- setting options for data jobs
- creating a simple data job
ACT: Data Enrichment
- identifying functionality that is available in the Quality grouping of nodes
- standardization, parsing, and casing
- identification analysis and right fielding
ACT: Entity Resolution
- understanding the data enrichment data sources
- working with address verification in a data job
- discussing the concept of match codes
- describing the process of generating match codes
- creating match codes
- clustering records
- adding survivorship to the entity resolution job
- defining business rules
- adding a business rule and an alert to a profile
- creating a historical visualization
- using a business rule in a data job
- data jobs with monitoring tasks
- multi-input/multi-output data jobs
- using data job references within a data job
- woriking with the Data Management server