Sifflet's Data Sharing feature has been enhanced with a new Transformations table. This table offers comprehensive metadata on all transformations cataloged in Sifflet, including dbt models, Fivetran syncs, and more. The table also provides the necessary join keys to connect each transformation to the dataset(s) it produces.

We’re thrilled to announce a groundbreaking evolution in how monitor statuses work in Sifflet! This release introduces dynamic statuses, bringing greater precision and flexibility to your monitoring processes. Here’s all you need to know:

✨ Dynamic Monitor Statuses

Monitor statuses are now dynamically calculated based on the qualification of detected anomalies, enabling smarter and more responsive monitoring.

🆕 New Monitor Statuses

We’ve introduced a new, streamlined set of statuses for monitors:

• Passing: Assigned when all data points are OK or qualified, meaning no unresolved anomalies.
• Failing: Applies when at least one anomaly remains unqualified.
• Needs Attention: Merges the previous Requires Your Attention and Technical Error statuses into one, clearly signaling monitors that need immediate action.
• Not Evaluated: Applies to monitors that have never been executed.

📌 Special Considerations for Data Points

• Obsolete Data Points: A data point marked as obsolete is treated as a qualified data point. It does not cause a monitor or group to enter a failing status. Only incremental monitors may then see their status affected by past unqualified data points, as for full dataset scan monitors, past data points are automatically qualified as obsolete.
• Evaluated Data Points: Only data points created since the first execution of the monitor are considered in the status evaluation.
  Historical data points generated during the first execution that have an abnormal status are automatically marked as obsolete. These obsolete data points will not affect the status of the monitor or group.

🌐 Multi-Dimensional Monitor Groups with Dynamic Statuses

Multi-dimensional monitor groups now have dynamic statuses similar to monitors, ensuring alignment and consistency.
A group’s status reflects the qualification of its data points:

• Passing: All data points in the group are OK or qualified.
• Failing: At least one anomaly in the group is unqualified.
• Needs Attention and Not Evaluated statuses are also applied to groups, mirroring monitor behavior.

⚡ Best Practice: Qualify Anomalies in Batches

To streamline the process of updating statuses, we recommend using the “Qualify All Anomalies” button at the group or monitor level. This allows you to handle anomalies in bulk, efficiently driving statuses from failing to passing.

🔮 What’s Next?

We’re developing status propagation between anomaly qualifications and incidents, ensuring that the statuses of monitors and the incidents they are associated with remain synchronized. This will eliminate the manual disconnect between incidents and monitors, enabling faster and smarter incident resolution.

The newly-released dbt tab now surfaces yet more dbt metadata, including detailed model information and all your custom meta properties:

• In the "Details from dbt" section:
  • Model name: If you use an alias, this field will contain the model name as it appears in your dbt project.
  • Description: The model's description.
  • Source: The Sifflet source via which the model was added to Sifflet. (And when the source was last refreshed.)
  • Project: The name of the dbt project.
  • Type: Reflects the dbt asset type (model, seed, snapshot, etc.).
  • Asset group: Reflects the dbt asset's group.
  • Asset access: Reflects the access configuration for this model.
  • Materialization: Reflects the materialization configuration for this model.
  • Deprecation date: The deprecation date of the model.
• In the "Metadata from dbt" section:
  • All the custom metadata you add to your dbt YAML files via the meta config.

Soon, you'll be able to leverage this enhanced dbt metadata across Sifflet to filter assets within the catalog and create monitors in bulk - so stay tuned!

App version: v409

Up until now, you first had to search for assets of interest before selecting them to assign them to a domain. This process could, depending on the number of assets in your domain, be quite time consuming. Domains also required a manual (or programmatic) action to be updated, which could make the maintenance cumbersome in some cases.

A brand new asset selection method was introduced to address these challenges: filter conditions-based domains.

• Filter conditions-based domains allow you to assign assets to your domains solely based on the source they come from and/or inherited tag/label they are tagged with, without having to select assets individually. You can now for instance assign all the assets coming from the SALES schema to the Sales Sifflet domain.

• Filter conditions-based domains also come with an automatic update concept: filter-conditions based domains indeed automatically pick up new assets that correspond to your domain conditions. For instance, if a new SALESREP table gets added to the SALES schema, yourSales Sifflet domain automatically updates to include this new SALESREP table.

Filter conditions method greatly simplifies domain creation and maintenance, making user experience and access controls much simpler to manage for your teams.

Read more about domains

App version: v409

We're excited to announce that Sifflet now integrates with Qlik Sense and Qlik Cloud Analytics! 🎉

Thanks to this new integration, Qlik users can now benefit from the multiple features of Sifflet's BI integrations, namely:

• Catalog your Qlik apps and enrich them with Sifflet metadata (owners, descriptions, tags, etc.).
• Access the upstream lineage of your Qlik assets to evaluate the impact of data incidents and determine the quality of the data/metrics in the BI layer.
• Create monitors upstream of Qlik assets to ensure that data incidents are detected before the data reaches your dashboards.
• Have real-time access to the impact of upstream data incidents on a given dashboard or report without leaving Qlik, thanks to the Sifflet Insights Chrome Extension.

Ready to get started? Visit our integration setup guide or contact your customer success manager for more details!

Want to see it live? Reach out for a demo!

App version: v400

Sifflet AI Assistant now allows you to automatically generate clear, concise, and contextually relevant descriptions for your data assets in one-click (previously only feasible at the column level). This feature streamlines documentation efforts, reduces manual workload, and ensures consistency in how assets are described across your organization.

This enhancement was designed to help your teams quickly understand and communicate the purpose, structure, and key attributes of data assets, empowering better collaboration and faster decision-making. Whether you're onboarding new team members, improving data cataloging, or accelerating incident resolution, AI-powered asset descriptions save time and provide immediate clarity—making your data more accessible and actionable.

Read more about generating metadata for your assets

App version: v400

Tiny but long overdue change: the Microsoft Teams integration got a facelift and now looks brand new. We took this revamp as an opportunity to also allow your team to:

• Send test notifications on your configured channels to make sure they still work properly
• Delete existing channels to keep your integration clean from outdated channels.

Read more about Microsoft Teams integration

App version: v395

Data catalog asset pages now feature much more detailed monitoring insights about your tables and their associated columns. These insights make it easy for your teams to get a grasp of your assets' health status in one glance: is the number of rows coming in for this table expected? Is the data fresh? Has there been any changes in the schema that could impact downstream assets? Are there any format weirdnesses in columns? Etc.

Having clear asset health statuses along with details about key monitoring dimensions is critical to ensure data trust among your team and can also be a great starting point to an investigation when noticing unexpected behaviors.

Read more about asset pages

App version: v393

Sifflet now fully supports Dynamic Tables within our Snowflake integration. With this enhancement, Dynamic Tables will:

• Be included in the Sifflet catalog (and can be monitored)
• Appear in the lineage graph for greater visibility

To enable Dynamic Tables in your Snowflake sources, ensure the following permissions are granted to the role used by Sifflet:

GRANT SELECT ON ALL DYNAMIC TABLES IN SCHEMA <DATABASE_NAME>.<SCHEMA_NAME> TO ROLE <SIFFLET_ROLE>;
GRANT SELECT ON FUTURE DYNAMIC TABLES IN SCHEMA <DATABASE_NAME>.<SCHEMA_NAME> TO ROLE <SIFFLET_ROLE>;
GRANT VIEW LINEAGE ON ACCOUNT TO ROLE <SIFFLET_ROLE>;

App version: v396

We're excited to announce that Sifflet now integrates with MicroStrategy! 🎉

Thanks to this new integration, MicroStrategy users can now benefit from the multiple features of Sifflet's BI integrations, namely:

• Catalog your MicroStrategy dashboards and reports and enrich them with Sifflet metadata (owners, descriptions, tags, etc.).
• Access the upstream lineage of your MicroStrategy assets to evaluate the impact of data incidents and determine the quality of the data/metrics in the BI layer.
• Create monitors upstream of MicroStrategy assets to ensure that data incidents are detected before the data reaches your dashboards.
• Have real-time access to the impact of data incidents on a given dashboard or report without leaving MicroStrategy, thanks to the Sifflet Insights Chrome Extension.

Ready to get started? Visit our integration setup guide or contact your customer success manager for more details!

Want to see it live? Reach out for a demo!

App version: v395