AuDatacy Data Quality & Observability
AuDatacy is a reseller of DQOps. DQOps is an open-source, end-to-end data quality & observability platform, suitable for use cases that include:
- Data Profiling
- Data Validation
- Data Reconciliation
- ETL Testing Automation
- Anomaly Detection
- Schema Drift
- Cloud Migration
... and even more. We are audaciously affordable data quality and data observability.
UX Designed for Each Data Team Member
Data Engineers and Data Stewards perform different functions. Data engineers need a data quality platform that is integrated into data pipelines. When the data platform matures from development to production, the non-coding operations team and business users need to observe data quality and detect/monitor anomalies. Each team has a user experience tailored to their needs.
DQOps provides multiple interfaces for every type of user.
- Use the user interface locally to configure checks and review table statuses.
- Integrate data quality checks into data pipelines by calling Python or REST client.
- Power users can configure data quality checks at scale using a command line.
Advanced Data Profiling
Start with a simple statistical analysis to get a quick insight into data. Then, run advanced profiling to choose the right data checks to monitor.
- More than 150 built-in table and column data quality checks
- Measure completeness, timeliness, validity, consistency, reasonableness, and accuracy
- Create custom data quality checks and rules with Jinja2 and Python
Custom Quality Checks
The DQOps platform is fully extensible. You can turn any SQL into a templated data quality check.
- Detect any data quality issues that business users want.
- Apply machine learning to detect anomalies.
- Any custom checks you create will be visible in the user interface.
Anomaly Detection
Automatically observe your data to detect potential data issues as soon as they appear and before anyone else is impacted.
Apply data observability to detect changes in:
- data volume,
- data characteristics min, max, mean and sum,
- missing or not fresh data,
- schema drifts.
Manage Incident Workflows
Keep track of the issues that arise during data quality monitoring. Automatically group similar data quality issues into data quality incidents.
- View, filter and manage the incidents
- Assign issues to respective teams
- Automate incident notifications
Code First
Proactively manage data quality without disrupting your existing workflows.
- Integrate data quality checks into data pipelines by calling DQOps.
- Run data quality checks from data pipelines using a Python client.
- Automate any operation visible in the user interface.
Monitor Virtually Any Data Source
Run data quality checks as customizable SQL query templates.
Query the results of your existing custom data quality checks and import them into the data quality warehouse to integrate them into the data quality KPI.
Design any data quality check that can detect business-relevant data quality issues.
