We're excited to announce a major upgrade to our Conditional monitors! We've removed their previous limitations, bringing them in line with all other Sifflet monitors (like Format Validation).

Conditional monitors now support the full suite of Sifflet's powerful monitoring features. This update simplifies monitor configuration and unlocks new data quality use cases.

Key capabilities now available for Conditional monitors:

• Advanced Joins: Easily join on more than one dataset using Sifflet's standard "join-everywhere" logic, replacing the old custom join code.
• Incremental Scans: Optimize your monitor runs by scanning only new or changed data.
• Full Feature Parity: You can now use group_by, where, and threshold settings to build more specific and powerful conditional checks.

What this means for you:

• For existing monitors: You can start updating your current Conditional monitors today to leverage these new configurations.
• For Data Quality as Code (DQaC): The new, expanded configuration is fully supported in our DQaC syntax.

App Version: v567

The Domain Management APIs empowers you to perform full lifecycle management of domains — from creation to deletion — enabling smoother integrations and automation of domain-related operations.

Available Endpoints:

GET /domains — Retrieve the list of all configured domains.
POST /domains — Create a new domain.
DELETE /domains/{id} — Delete a domain by its unique ID.
GET /domains/{id} — Retrieve details for a specific domain.
PATCH /domains/{id} — Update the configuration or metadata of a domain.

💡 Why is this useful?

These new APIs allow developers and platform integrators to manage domains directly through automation or external workflows — without needing to use the Sifflet UI.

We are working on integration domain in our terraform provider that will be available in a few weeks.

We’re excited to introduce the Google Chat integration for Sifflet Webhooks!
This new feature brings real-time data quality notifications directly into your Google Chat spaces — helping your teams stay informed and take immediate action when issues arise.

With this integration, you can seamlessly connect your Sifflet monitors and pipeline alerts to Google Chat, ensuring that data quality events never go unnoticed.

The new integration empowers you with:

Real-Time Data Quality Notifications: Receive instant alerts in your Google Chat spaces whenever data quality issues occur — including monitor failures, status changes, or transformation run errors — ensuring your team stays informed at all times.

Seamless Setup and Management: Connect Sifflet to any Google Chat space through a simple webhook configuration. You can easily test, verify, and manage your Chat connections directly within Sifflet’s Collaboration Tools.

Centralized Communication for Data Quality Events: Bring all your data quality alerts into your existing Google Chat workflows, enabling faster triage, better collaboration, and improved visibility across teams.

Ready to connect Sifflet to Google chat ? Check out our detailed documentation available here.

App version: v566

You can now connect Sifflet directly to Databricks Workflows to gain complete, end-to-end observability of your data pipelines and the assets they generate. This new integration allows you to monitor your data orchestration alongside your data quality in a single platform.

Key features include:

• Automated Job Discovery: Sifflet now automatically discovers your Databricks jobs and populates them as assets in the Sifflet Data Catalog. Each job asset page centralizes key metadata, including run status, tags, and ownership.

  

• Enhanced Data Lineage: See exactly which Databricks Workflows jobs are creating or updating your tables with an enriched, end-to-end lineage view. This helps you understand the impact of pipeline changes and troubleshoot issues faster.

  

• Coming Soon: AI-Powered Root Cause Analysis: By understanding the relationship between your jobs and data, Sifflet's AI assistant, Sage, will be able to identify failing Databricks jobs as the root cause of data incidents.

To get started, please refer to our new Databricks Workflows documentation.

App version: v564

We're excited to announce the launch of Sentinel, our new AI-powered agent designed to automate and accelerate the creation of data quality monitors. Sentinel intelligently analyzes your data assets and recommends a comprehensive set of monitors, helping you achieve full coverage in minutes, not hours.

Say goodbye to manual configuration and guesswork. Sentinel understands your data's context to suggest the most effective monitors for your specific needs.

What's New:

• AI-Powered Recommendations: Sentinel analyzes data samples and metadata to suggest a wide range of monitors, including checks for format, uniqueness, value ranges, logical consistency (shipping_date > order_date), and more.
• Three Powerful Workflows: You can now access Sentinel from wherever you work:
  • On a Single Asset: Generate recommendations directly from any asset page for quick, targeted coverage.
  • In Bulk from the Data Catalog: Select up to 10 assets at once from the catalog to apply consistent monitoring at scale.
  • Across an Entire Data Product: Ensure comprehensive monitoring for all assets within a Data Product with a single click.
• Streamlined Creation Process: A simple, guided flow allows you to review all AI suggestions, select the ones you want, and create them in a single action.

Sentinel helps you save time, discover hidden data quality issues, and ensure your data assets are always reliably monitored.

➡️ Read the full documentation to get started with Sentinel

We've updated the Mute button to make your life easier! Now, when you mute a monitor, it will automatically unmute itself the next time its status changes.

This lets you silence temporary noise without worrying about forgetting to turn notifications back on. As always, you can still manually unmute at any time.

App version: v557

Impact: response payload of the following endpoints

• Get list of users: GET https://{tenant}.siffletdata.com/api/v1/users
• Create a user: POST https://{tenant}.siffletdata.com/api/v1/users
• Update a user: PATCH https://{tenant}.siffletdata.com/api/v1/users/{id}
• Get a user by id: GET https://{tenant}.siffletdata.com/api/v1/users/{id}

The response payload for those endpoints contains a new property named status indicating if a user is enabled or disabled. This property is represented as an enum string with two possible values: ENABLED and DISABLED.

Example of the new response payload

{
   "id":"80807519-9b52-4c6c-88b1-3945e9b35a2e",
   "name":"Roger",
   "email":"[email protected]",
   "role":"EDITOR",
   "permissions":[
      {
         "domainId":"aaaabbbb-aaaa-bbbb-aaaa-bbbbaaaabbbb",
         "domainRole":"EDITOR"
      }
   ],
   "authTypes":[
      "LOGIN_PASSWORD"
   ],
   "status":"ENABLED"
}

We're excited to introduce a major enhancement to your security and access management: role-based domain control for Access Tokens.

This update gives you a new level of precision, allowing you to assign each token to one or more specific domains. You can then grant, for each domain, one of four distinct domain-level roles: Viewer, Editor, Catalog Editor, or Monitor Responder.

This ensures that every token operates on the principle of least privilege, granting only the exact permissions needed. The result is a more secure, flexible, and manageable system for your entire team.

What about my existing tokens?

For a seamless transition, your existing Access Tokens will continue to function as they do today, with access to all domains. You can now edit any of these tokens at your convenience to restrict their access to specific domains and assign them a precise role.

App version: v552

We've enhanced our debugging capabilities for incremental monitors. Previously, the "Show Failing Rows" button provided a view of the failing rows for the latest monitor run. Now, the feature is datapoint-specific.

This means you can select any point from a monitor's execution history and see the exact rows that caused a failure for that specific time and execution. This makes it much easier to investigate if a problem from a past run has been resolved or to analyze a specific incident in isolation.

App version: v542

We're thrilled to announce a fundamental redesign of how sources are managed in Sifflet. We've transitioned from a schema-by-schema approach to a unified, environment-level model. This update streamlines the user experience, provides a more accurate representation of your data landscape, and introduces powerful new tools for managing and troubleshooting your integrations.

✨ New Features & Major Improvements

• Environment-Level Sources: Sources are now managed at the environment level. For example, your entire Snowflake account or BigQuery project is now represented as a single, consolidated source in Sifflet. Your existing sources have been automatically migrated to this new structure.

• New Source Details Modal: Clicking on any source name now opens a powerful details modal. This new view provides a granular breakdown of all the schemas within that source and their individual statuses.

• Granular Metadata Refresh: You now have precise control over what you refresh. Alongside the main "Run" button for a full source sync, you can now trigger refreshes for specific schemas or databases directly from within the new details modal.

• Streamlined Failure Resolution: Troubleshooting connection issues is now faster than ever. The Source Details modal includes:

  • A "Failures" Tab: This view automatically lists only the schemas that failed to sync, so you can immediately see what needs attention.
  • "Rerun All Failures" Button: Trigger a targeted refresh for all failed items with a single click.
  • Per-Schema Logs: A "Logs" button appears next to each failed schema, giving you instant access to detailed error messages to diagnose the root cause.

• Official Source Merging Process: We've introduced a clear, safe process for consolidating multiple sources into a single primary source—perfect for cleaning up your setup after the migration. Sifflet seamlessly maps all monitors and assets during the merge, ensuring no loss of data or observability.

🗒️ For API & Terraform Users

• API Deprecation: Please be aware that the legacy API endpoint used for managing schema-level sources is now deprecated and will be decommissioned in a future release.
• New API Endpoint: A new, more powerful API endpoint for managing environment-level sources is now available. We strongly encourage you to migrate your scripts and Terraform configurations to use the new endpoint. Please refer to our API documentation for full details.

We are confident these changes will make managing your data integrations in Sifflet a much more intuitive and efficient experience. For additional details on the new source management experience, refer to the dedicated documentation page.

App version: v531