DQOps

Define data quality requirements from the data engineering perspective

The Data Engineering Team is responsible for managing the data warehouse or the data lake. The data quality issues of the incoming data may affect the stability of the data pipelines.

Well-designed data warehouses, ETL tools, and data pipelines report the progress of data pipelines and errors as logs. However, to fully understand the complexities of data processing, it is necessary to have a bird's eye view of the entire process.

This stage describes the key steps for gathering the data engineering requirements in the data quality area, introducing the data engineers to the data quality tool, and connecting the data quality tool to the data platform.

Learn more about the efficient data quality process in our Ebook (https://dqops.com/best-practices-for-effective-data-quality-improvement/).

Develop and deploy custom data quality checks.

A customizable data quality platform (such as DQO) should support the use of custom data quality check definitions, provided as custom SQL queries or implemented as a custom code.

Custom data quality checks should be reusable across tables. Custom
checks should not be hard coded for individual tables. To enable the reusability of custom data quality checks, The following example is a template of a data quality sensor that is counting rows with a non-negative column value.

Learn more about the efficient data quality process in our Ebook(https://dqops.com/best-practices-for-effective-data-quality-improvement/).

As data complexity increases, the importance of has never been greater. But getting started with can be challenging. Use our best practices to remove all data quality issues and reach a 100% data quality score.

Download our eBook to learn how to:
Set up a data quality monitoring process.
Define requirements for both a business and data engineering perspective.
Measure data quality across multiple dimensions, such as accuracy, validity, completeness, consistency, currency, or timeliness.
Detect and respond to data quality problems.

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