Minor release: some bugfixes

🛠 Fixes

• Fixed a bug when ingesting some BigQuery tables
• Fixed various display issues

App version: v116

Release: New User Interface

✨ New & Improved

• New User Interface: The new user interface (UI) ensures a cleaner look and feel for technical and non-technical users. Among the key changes are a new theme, updated iconography, and a more straightforward wording for monitors (formally called data quality rules).

🛠 Fixes

• Fixed a bug creating latencies when loading incidents on the incident page

App version: v115

Minor release: monitors now support epoch fields

✨ New & Improved

• Monitoring: supporting monitoring based on EPOCH fields for anything time-related monitoring

App version: v114

Release: Updated Dashboard and new custom ML-based monitor

✨ New & Improved

• Dashboards: Users can now have better visibility on the monitoring coverage and conduct analysis on their incidents and the time to resolutions.
• Metric observability: ML-based custom rules: Users can now observe and monitor business metrics with custom SQL queries and leverage Siffle ML capabilities to detect anomalies and adjust to future behaviors automatically.

🛠 Fixes

• Fixed a bug where some rules could not run on Snowflake tables

App version: v113.1

✨ New & Improved

• Domains: This feature will allow organizations to enable reliable data democratization by creating and managing business domains on the platform. A business domain represents a group of data assets belonging to a business area, such as finance, sales, or operations. It allows the users within these business domains to have the right visibility on their data assets and efficiently collaborate with other team members. You can find more information here.
• New monitoring capability, introducing conditional rules: Business users can now monitor their data based on their specific business context without having to write complex SQL queries. Sifflet currently supports BigQuery data assets; other data sources will be progressively rolled out.
• Monitoring Rule Distribution Change improvement. Users can now choose between the static or dynamic type of monitoring (you can find more information here)
  • Dynamic: the rule fails if the statistical test finds any anomaly based on the previous trends, there is no need to define any threshold.
  • Static: the rule fails if one category's distribution changes more than a given threshold.
• Snowflake integration improvement, supporting Snowflake streams: Users can view streams on the Sifflet catalog.
• BigQuery integration improvement, supporting nested fields: Users can now view their nested fields on the Sifflet catalog and implement monitoring rules on them.
• BigQuery integration improvement, supporting Billing Project id: Users can now specify the Billing Project Id (optional) of their choice for BigQuery connections.

🛠 Fixes

• Fixed a bug where the scope of Incidents would sometimes not display properly.

App version: v112

✨ New & Improved

• 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

✨ New & Improved

• 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

✨ New & Improved

• 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

✨ New & Improved

• 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

✨ New & Improved

• 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