Informatica Data Quality Tutorial

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Informatica Data Quality Fundamentals:

This tutorial gives you an overview and talks about the fundamentals of Informatica Data Quality (IDQ).

  • Informatica Data Quality is a suite of applications and components that you can integrate with Informatica Power Center to deliver enterprise-strength data quality capability in a wide range of scenarios.
  • The core components are Data Quality Workbench, Data Quality Server.
  • Use to design, test, and deploy data quality processes, called Data Quality Workbench plans. Workbench allows you to test and execute plans as needed, enabling rapid data investigation and testing of data quality methodologies.
  • Data Quality Server. Use to enable plan and file sharing and to run plans in a networked environment. Data Quality Server supports networking through service domains and communicates with Workbench over TCP/IP.
  • Both Workbench and Server install with a Data Quality engine and a Data Quality repository. Users cannot create or edit plans with the Server, although users can run a plan to any Data Quality engine independently of Workbench by runtime commands or from PowerCenter.
  • Users can apply parameter files, which modify plan operations, to runtime commands when running data quality plans to a Data Quality engine.
  • Informatica also provides a Data Quality Integration plug-in for PowerCenter. This plug-in enables PowerCenter users to add data quality plan instructions to a PowerCenter transformation and to run the plan to the Data Quality engine from a PowerCenter session.

In Data Quality, a plan is a self-contained set of data analysis or data enhancement processes. A plan is composed of one or more of the following types of component:

  1. Data sources provide the input data for the plan.
  2. Data sinks collect the data output from the plan.
  3. Operational components perform the data analysis or data enhancement actions on the data they receive.

If you want to enrich your career and become a professional in Informatica Data Quality, then visit Mindmajix - a global online training platform: "Informatica Data Quality Training" This course will help you to achieve excellence in this domain.

Informatica Data Quality Infographics:

 Informatica Data Quality Tutorial

Role of Dictionaries

Plans can make use of reference dictionaries to identify, repair, or remove inaccurate or duplicate data values. Informatica Data Quality plans can make use of three types of reference data.

Standard dictionary files: These files are installed with Informatica Data Quality and can be used by several types of components in Workbench. All dictionaries installed with Data Quality are text dictionaries. These are plain-text files saved in . DOC file format. They can be created and edited manually.

Database dictionaries: Informatica Data Quality users with database expertise can create and specify dictionaries that are linked to database tables, and that this can be updated dynamically when the underlying data is updated.

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Third-party reference data: These data files are provided by third parties and are provided by Informatica customers as premium product options. The reference data provided by third-party vendors are typically in database format.
For more information about Informatica, please refer to https://www.informatica.com

We have compiled a few more articles to get you acquainted with the Informatica Data Quality course. We will cover these in-depth in our Informatica Data Quality online training sessions.

List of Informatica Courses:

Mindmajix offers training for many other Informatica courses depends on your requirement:

Course Schedule
NameDates
Informatica TrainingNov 02 to Nov 17View Details
Informatica TrainingNov 05 to Nov 20View Details
Informatica TrainingNov 09 to Nov 24View Details
Informatica TrainingNov 12 to Nov 27View Details
Last updated: 05 Jan 2024
About Author

Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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