Open-source Data Quality and Data Observability platform that works at all stages of a data platform project. Detect table schema changes, invalid data, data timeliness or completeness issues, or anomalies in time series. You can define data quality checks as YAML data contracts, having code completion and validation in VS Code. After your data platform moves to production, your users and operations team can use the UI to manage data quality checks and incidents without breaking the code.

DQOps is a platform that makes it easy to profile new data sources (profiling a CSV file is shown in the video). After profiling and finding first data quality issues, you can let DQOps run data observability checks to monitor your data souces every time. You can integrate checks into data pipelines and edit data contracts in YAML with code completion in VSCode. When you move your project to production, your operations team can use the UI to manage data quality checks and get notifications of data quality incidents such as schema changes, anomaly detection, or timeliness issues. You can even define custom data quality checks with Jinja2 templates, define custom rules or even create custom data quality dashboards if somebody asks you how you are progressing with data quality. Definitely check out the documentation at: https://dqops.com/docs/ , it shows how to detect all sorts of common data quality issues.

Just spotted your launch on Product Hunt for DQOps Data Quality Operations Center - looks fantastic! Congrats on the big day, wishing you all the success with it!

DQOps Data Quality Operations Center

Open-source data quality with code-first and no-code support

Open-source data quality with code-first and no-code support