DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. Founded in 2020, DQO is headquartered in Warsaw, Poland.
DQO is the company behind DQOps, an open-source data quality platform designed for data quality and data engineering teams. The platform makes data quality visible to business sponsors by providing an interface for adding data sources, configuring data quality checks, and managing issues. DQO was founded in 2020 and is headquartered at Konstruktorska 11, Warsaw, 02673, Poland.
DQO operates with a team of fewer than 49 employees. According to its DesignRush profile, the company's minimum project budget is $25,000 to $50,000, with an average hourly rate of $75 per hour. DQOps is available both locally and as a SaaS platform.
DQOps includes more than 150 built-in data quality checks, and teams can design custom checks to detect business-relevant issues. The platform supports incremental data quality monitoring for very large tables and offers built-in or custom dashboards for tracking data quality KPI scores. This allows data teams to show business sponsors measurable progress in improving data quality.
The platform is DevOps-friendly: data quality definitions are stored as YAML files in Git, checks can run directly from data pipelines, and actions can be automated through a Python client. DQO lists Big Data Analytics among its services, and its published portfolio covers DQOps product screens, including the checks editor, completeness issues dashboard, incidents management, KPIs scorecard dashboard, profiling, and data sources views.
DQO's disclosed clients include one of the biggest FMCG companies and a company from the public health sector, both undisclosed. No Google reviews were available on the profile at the time of publication.
Services
- Big Data Analytics
- Data Quality Monitoring
Industries Served
Team Size
Pros
- Open-source platform with over 150 built-in data quality checks and support for custom checks
- DevOps-friendly with YAML-based definitions in Git, pipeline execution, and a Python client
- Deployable locally or as a SaaS platform