DQOps Data Quality Operations Center
Open-source data quality with both code-first and no-code
Listed in categories:
GitHubDataData & Analytics
Description
DQOps is an open-source data quality platform that helps in profiling new data sources, automating data quality checks, and detecting issues with Data Observability. It offers more than 150 built-in data quality checks and allows for custom data quality rules and checks using Jinja2 and Python.
How to use DQOps Data Quality Operations Center?
Connect data sources, start monitoring data, integrate data quality checks into pipelines, and measure data quality with KPI scores.
Core features of DQOps Data Quality Operations Center:
1️⃣
Self-service data profiling
2️⃣
Integrating data quality checks into data pipelines
3️⃣
Detecting and managing data issues
4️⃣
Measuring data quality KPI scores
5️⃣
Anomaly detection
Why could be used DQOps Data Quality Operations Center?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Automating data quality checks | ✅ | |
| # 2 | Monitoring data quality in data pipelines | ✅ | |
| # 3 | Detecting schema changes and anomalies | ✅ | |
Who developed DQOps Data Quality Operations Center?
DQOps is developed by a team of data quality experts who aim to provide a comprehensive solution for improving data quality operations.