New monitoring capability, Interlinked metrics: Users can now monitor the consistency of their metrics across different data assets and get alerted in case of any deviation. See here for more information.
Lineage enhancement: We have improved the lineage between data warehouses and Looker assets, and added an undo button to roll back the expanded lineage card when navigating on the graph.
Slack Private Channels: Users can now send notifications on rules failures on Private Slack Channels. See here for more information.
Microsoft Teams: Users can now send notifications on Microsoft Teams. See here for more information.
🛠 Fixes
Fixed an issue where the whitelisted domain for email notifications was not displayed
Incidents logic improvement: an incident is now created only when there is not any open incident associated to the same rule. If there is an open incident, the existing one will be updated instead. Additionally, you can now see more information on an incident such as the number of failures since the ticket was opened.
New Monitoring template: Users can now add their own custom regular expression to catch data not matching a given pattern. The template can be found in Category "Format Validation" -> Template "Matches Regex"
🛠 Fixes
QuickSight integration: fixed a bug preventing to connect to QuickSight in some cases
Distribution Rule: fixed a bug where the rule could not run on some large numbers
SQL Custom Rule: fixed a bug where the rule could not display the failing rows for some queries
Catalog usage: We have extended our Usage feature to support tables and views on Snowflake. Snowflake users can now understand their most queried data assets for better monitoring and troubleshooting management.
Firebolt integration improvement: Sifflet now see Views in Sifflet's catalog and monitor its data quality.
Lineage graph UX improvements
🛠 Fixes
Secret display: fixed a bug where the secrets were not displayed correctly and did not apply appropriately on some occasion
External tables on BigQuery: Users can now discover and monitor external tables on BigQuery.
Usage on Snowflake: We have extended our Usage feature to support tables and views on Snowflake. Snowflake users can now understand their most queried data assets for better monitoring and troubleshooting management.
Looker integration enhancement: We now support Looker derived tables for the lineage from the data warehouse to the Looker dashboard.
Firebolt integration improvement: Users can now choose which Firebolt engine Sifflet will use when querying the data to offer more flexibility.
Monitoring graphs improvements: Monitoring graphs include more details such as the run or the feedback timestamps. Users also can see the historized graph in past runs to get a better understanding of the trigger of the alerts in the past and better compare with actual values.
🛠 Fixes
Catalog search: fixed a bug where the filters did not apply properly on some occasion
Fixed a bug preventing from showing properly the lineage between Tableau and BigQuery
Intrahourly monitoring for data freshness: You can now monitor the freshness of your table at an intra-hourly frequency. For example, you can now be alerted when you expect a refresh every 10 min.
Emails as alerts destination: you can now send an alert to multiple email addresses at the same time when a data quality rule is failing.
ML-based monitoring setting: You can now use date fields with type string as the time field reference for all the ML-based monitoring.
New Integrations page: We have redesigned our Integrations page to surface more insights. For instance, we now display the status of the latest synchronizations and provide more details in case of a sync failure.
🛠 Fixes
Fixed a bug where time-series graphs would not display in some cases with the use of multi-dimensional monitoring
Fixed a bug where Tags could not be added to some data assets
Roles permissions update: We have restricted the access to the sources secrets to Admin roles only. As of now, accounts with the Editor role can no longer see the secrets.
Lineage improvement: We have improved our field-level lineage engines on Snowflake and BigQuery to cover additional use cases.
ML-based monitoring sensitivity: we have improved the behaviour of our ML models when setting the sensitivity parameter to low
New monitoring page: We have redesigned our monitoring page to surface more insights from the list view of the existing data quality rules and improve the user experience when interacting with them.
🛠 Fixes
Fixed ingestion issues that can be encountered with BigQuery and Looker integrations.
LookML repository SSH and token connection. You can now connect to your LookML repository using both SSH or a token
New Rule status "Requires your attention": this new status will appear when there is an issue in your rule setup that requires your attention. For instance, running a rule requiring a date column on a table that doesn't have one will result in this status.
Freshness rule new graph: Freshness graph is now different than the completeness graph and provides clearer information
🛠 Fixes
Fixed a bug where the graph will not show while using the multi dimension analysis (group by) in some edge cases
