v1.3.0

1.3.0

• Screens that show a list of tables and the data quality status divided by data quality dimensions
• Analyze nested values in JSON fields
• Analyze files on Azure storage buckets, using the az:// prefix.

v1.2.0

1.2.0

• Small bug fixes
• Small layout changes (fonts)
• Updates in the documentation
• MySQL and Snowflake drivers upgraded
• Column list shows the status of data quality dimensions
• Corrections of queries for mysql, oracle
• The platform tracks the data quality status of tables and columns

v1.1.0

1.1.0

• Default data quality checks are activated automatically by using table and column search filters, now called as data quality check patterns
• Support analyzing CSV, JSON and Parquet files directly using DuckDB that is now bundled into DQOps
• Fixes in supporting dots and other special characters in the table names
• Incidents are not created for profiling checks, only for monitoring and partitioned checks

v1.0.2

1.0.2

• Changes to the default configuration of the table availability check
• Use configurable connection pooling when connecting to DQOps Cloud
• Documentation updates - categories of checks documented.
• Handling null values in accepted_values checks.
• Boolean percentage checks support both the minimum and maximum percentage.
• Incident details shows the histogram correctly in all cases, also avoiding extra calls to the backend.

v1.0.1

1.0.1

• Data dictionaries
• More efficient watching for running jobs in the notification panel
• SingleStoreDB connector based on MySQL with integration tests
• Custom SQL check for importing data quality results from third-party data quality libraries
• Small corrections in the documentation
• Pip startup script supports mirrors and retries when downloading the release package
• MIN and MAX anomalies checks
• Improvement in the performance of the notification panel

v1.0.0

1.0.0

• Apache Spark connector
• Databricks connector
• Initial versions of Apache Trino and Presto connectors
• Updates to dashboards - cross-filtering configured
• Refactoring towards supporting multiple data domains at the same time
• Fixes in the UI - table status overview screen, filtering checks related to incidents
• Support activating and deactivating multiple data quality checks from UI
• Additional navigation screens for jumping between schemas and tables
• COMPLETE DOCUMENTATION with examples

v0.4.1

0.4.1

• More Airflow operators - waiting for a data quality job to finish
• Table quality status summary screen
• Renames of some dashboards
• Updates to dashboards - unification of filters
• Current table status detects statuses at column and check levels, also returning the current and highest historical quality status
• Fixes in the UI - filtering detailed check results

v0.4.0

0.4.0

• Data quality dashboards reorganized, layout unified
• Python client and REST API documentation
• Additional Airflow operators
• Small bug fixes in the UI
• DQOps concepts documentation

0.3.0

• Improvements for running DQOps as a cloud hosted SaaS platform
• Reorganized data quality dashboard tree to match the structure of checks
• UI screens for managing the configuration: users, default schedules, notifications and more
• Airflow operator for running checks

0.2.1

• Table comparison support
• Anomaly detection checks renamed
• Bug fixes

0.2.0

• Reorganization of all data quality checks
• User interface created
• Documentation created for all checks
• Extensive REST API for all operations
• Additional connectors for PostgreSQL, MySQL, SQL Server, Oracle

0.1.0

• Initial command-line version (preview release)

v0.3.0

• Table comparison support
• Anomaly detection checks renamed
• Bug fixes

v0.2.1

• Table comparison support
• Anomaly detection checks renamed
• Bug fixes