Last updated: February 05, 2024

Working with DQOps

Our guides provide detailed step-by-step instructions on how to use DQOps to effectively monitor and manage data quality issues.

List of Working of DQOps guides

Guide name	Description
Daily monitoring of data quality	Understand the daily workflow of using DQOps to review recently detected data quality issues.
Basic data statistics	Learn how to collect basic statistics about data sources during the data profiling stage, before activating advanced profiling checks.
Run data quality checks	Familiarize yourself with the DQOps user interface and command line to run data quality checks.
Review the data quality results on dashboards	Learn how to use detailed data quality dashboards in DQOps for drilling down to identify all tables and columns affected by issues.
Delete data quality results	Learn how to delete a subset of outdated data quality results, especially when tables were decommissioned or checks were run by mistake.
Configuring the scheduling of data quality checks	Understand how scheduling works in DQOops and learn how to configure the scheduling of data quality checks at different levels..
Incidents and notifications	Understand how the data quality incident workflow works in DQOps, and how to use all incident management screens to manage data quality incidents.
Activate and deactivate multiple checks	Learn how to activate or deactivate multiple data quality checks in the DQOps user interface.
Set up data grouping	Understand how to configure data grouping in DQOps for running data quality checks for different data streams.
Compare tables	Understand how to compare tables between data sources using DQOps. Table comparison (reconciliation) enables detection of data accuracy issues.
User and access management	Learn how to manage user access in DQOps, to enable splitting the roles between data quality editors, operation teams, and data stakeholders.
Managing errors	Learn how to manage log errors and data quality check execution errors and where they are stored.
Creating custom data quality checks	learn how to create a custom data quality check in DQOps using the user interface.
Working with DQOps Shell	Learn how to use DQOps Shell both from a development instance started as a Python module, or started as a docker container.