✨ New & Improved

• Dashboard page revamp: Our dashboard page comes now with deeper insights and a more personalized experience for the user.
• Pinning: Users can now pin their most useful data assets, data quality rules, or incidents and easily navigate to them from the My summary page on the dashboard.
• "Assigned to" Filter in Incidents page: Users have an additional filter "Assigned to" which allow to filter your incidents on certain users
• 7 latest rule run Status: you can now see the 7 latest run status for each rule instead of the last one

🛠 Fixes

• Fixed a bug where rules will result in a technical error in few instances
• Fixed an issue where the role 'Viewer' was missing some access rights (e.g. access to the failing rows of a rule)

✨ New & Improved

• ML-rules data querying optimisation: Sifflet now optimises the data sources querying for the models training in your datasource by querying only the new data ingested since the last rule execution

🛠 Fixes

• Fixed an issue where rules ran with technical errors in a few instances
• Fixed an issue where the rule schedule will sometimes not update

✨ New & Improved

• Power BI integration (Beta): Supporting Microsoft Power BI as a bi integration. Sifflet will be able to catalog dashboards, tiles, and datasets as well as full lineage to the actual datasources.

• Bigquery integration test : advanced testing to make sure that Sifflet and Bigquery are properly configured.

• Dataset selector optimization : improved user experience for the dataset selector used in both filters and quality rule creation stepper.

• Metadata pulling optimization: performance optimization for the jobs pulling metadata from sources.

🛠 Fixes

• Graph misinterpretation when min, max, and predicted value are the same.
• Fixing the preview issue when trying to sample a Snowflake table.
• Display a comprehensive message when a multi-dimension rule fails because of a high dimension cardinality.

✨ New & Improved

• Multi-dimensional monitoring (Beta): Users can now monitor their data assets at a more granular level by breaking down the monitoring based on any dimensional/low-cardinality fields.
  This type of monitoring can be applied to any data quality template.
• Multiple UX improvements: including a better UX when deploying new versions of the product and a redesign of notifications cards and an improvement of the data quality rules monitoring page.

🛠 Fixes

• Fixed a bug on the catalog search preventing from considering the tags filter.

✨ New & Improved

• Multi-schema/datasets integration: Users can simultaneously integrate Sifflet to multiple schemas (and datasets in the case of BigQuery) of the same data source.
• Data quality rules batch editing users can simultaneously batch edit schedules, descriptions, tags, and alert destinations for multiple rules.
• Auto-coverage rules frequency: Users can now schedule Auto-coverage suggested rules with cron expressions.

🛠 Fixes

• Fixed bugs preventing the run of ML-based rules in some specific circumstances.

✨ New & Improved

• Data assets usage: Users can better understand their most critical data assets based on their usage. This feature ranks the data tables of the same data source based on the number of reading queries and gives the breakdown by the user account. This feature is GA on BigQuery only.
• Time offset: you can now insert a time offset for your monitoring rules in case of a constant time delay when your dataset gets updated. You can find more details here
• Slack and email alerting: we streamlined the way alert notifications are now sent. Previously only monitoring rules with criticality "Critical" will send notifications. All monitoring rules can send notifications as long as the destination is defined.
• UI/UX improvements: a new design for both the asset page and rule pages for a better navigation on the UI
• Performance optimization on connections to data warehouses and BI tool Integration Tableau

🛠 Fixes

• Fixed a display bug when hovering on data assets on the lineage view
• Fixed an issue that prevented the creation of a new user

✨ New & Improved

• SSO (Single Sign-On): you can now enable SSO on Sifflet. For more information, you can refer to this page
• CLI: you can now interact programmatically with Sifflet. For more information, you can refer to this page
• Monitoring new templates: 4 new templates have been added: "Duplicate Count", "Duplicate Percentage", "Null Count" and "Null Percentage". You can now monitor - with a dynamic threshold - the count or percentage of Null values in a field and the count or percentage of duplicates in a field. You can find these template in Category "Field Profiling"
• Several UX enhancements across the UI, such as ability to see the lineage directly in an incident report

🛠 Fixes

• Fixed a bug preventing some incidents to be created following a rule failure

✨ New & Improved

• Monitoring filters: users can now filter rules based on the data source, table name, monitoring type, or any tag added to the rules.
• Several UX enhancements across the UI, such as the lineage exploration experience on an incident report or pagination in data sources' historical runs.

🛠 Fixes

• Fixed a bug in the integrations page preventing from showing the next runs timestamps.

✨ New & Improved

• Incident report: Users can now efficiently troubleshoot data quality issues by having all the necessary context on the same page: Upstream and downstream dependencies of the asset impacted by the incident, Users assigned with the troubleshooting, incident mitigation updates ...

• Dynamic monitoring: define data quality rules on numerical values with dynamic thresholds, using ML models and fields historics.

• dbt Cloud: Sifflet integrates with dbt Cloud and extends all our dbt-specific features to users relying on dbt Cloud.
• Improved search engine performance by cutting the search time by up to 80%.
• Improved the connection testing for Snowflake when connecting a new data source.
• Improved the UI to ensure a smoother user experience

✨ New & Improved

• Introducing data assets SQL: Users can now preview SQL queries behind supported data assets directly on the UI with the Show SQL button. In the case of data tables, this will be the create/edit/update SQL query. In the case of dbt models, this will be the SQL queries the models are running.

🛠 Fixes

• Fixed an issue preventing users sometimes from expanding the upstream dependencies on the Lineage graph.