As a company founded and headquartered in France, supporting our users in French is part of our DNA. We're incredibly proud and excited to announce that the Sifflet user interface is now fully available in French!

Now you can navigate, monitor, investigate, and manage your data observability directly en français.

• How to Switch: Changing your language preference is easy:
  1. Click on your User Profile icon located in the top-right corner of the Sifflet application.
  2. In the dropdown menu, locate the "Select Language" / "Choix de la langue" selector.
  3. Choose between "English" and "Français". The interface will update immediately.

We hope this makes the Sifflet experience even more accessible and comfortable for our French-speaking users!

Streamline your workflows and quickly access your most common data views with the new Saved Filters feature! Instead of reapplying the same filters repeatedly, you can now easily save and reuse your filter configurations.

• How it Works:
  • Apply any combination of filters on a supported page (Catalog, Monitors, or Incidents).
  • Click the new "Save" button that appears once filters are applied.
  • Give your filter set a descriptive name.
• Reuse Instantly: Next time you visit the page, you can simply select your saved filter set from a list instead of manually configuring the filters again.
• Availability: This feature is now available on the Catalog, Monitors, and Incidents pages.
• Personalized (For Now): Saved filters are currently specific to your user account. We are exploring options to allow sharing saved filters with your team in a future update.

Save time and effort by creating personalized shortcuts to the data views you use most often!

App version: v473

We've significantly enhanced the Sifflet Data Sharing feature to provide you with more timely access to your monitoring and observability data.

• Faster Refreshes: By optimizing our internal processes using a Change Data Capture (CDC) pattern, shared data is now refreshed much more frequently. You can expect data updates every four hours, a significant improvement from the previous daily refresh cycle.
• snapshotted_at Column Behavior Change: Please note an important update regarding the snapshotted_at column in the shared tables. This column no longer reflects the timestamp of the overall refresh batch. Instead, it now accurately represents the timestamp of the last captured change for that specific row, providing more granular insight into when each record was last updated.

This update ensures you have more frequent access to the latest data available through the Data Sharing feature, reflecting changes closer to when they occur. For more details on our Data Sharing feature, review the dedicated documentation.

You can now automatically update your Sifflet incidents based on the progress tracked in ServiceNow. This feature synchronizes status changes from a linked ServiceNow incident directly to its corresponding Sifflet incident.

• How it works: When a linked ServiceNow incident's state transitions to specific 'In Progress' or 'Resolved/Closed/Cancelled' values, the corresponding Sifflet incident's status will automatically update to 'In progress' or 'Closed - Fixed' based on a mapping that you provide.
• Configuration required: To enable this synchronization, activate the "Enable status changes sync from ServiceNow to Sifflet" toggle within the Sifflet ServiceNow integration settings. Additionally, a specific Business Rule needs to be configured in your ServiceNow instance using the script provided in our documentation.
• Benefit: Ensures that incident progress and resolution tracked in ServiceNow are accurately and automatically reflected within Sifflet, improving visibility for users monitoring data quality incidents in Sifflet and reducing the need for manual status updates.

Learn more about configuring ServiceNow Incident Status Synchronization via the dedicated docs page.

App version: v472

This week we have released a new ML algorithm in the aim to reduce false positives.

What you should expect to see:

• A model that adapts faster to new data trends.
• Less false positives

This generative model is a big change from our previous time series forecasting model so please do let us know should things change as we are carefully observing and finetuning the model's performance.

We're thrilled to announce that Sifflet now integrates natively with ServiceNow! This integration brings Sifflet's data observability capabilities directly into your existing ServiceNow incident management workflow, ensuring that data quality issues are never missed.

The new integration empowers you with:

• Centralized Data Quality Incident Management: Integrate Sifflet with ServiceNow to ensure all data quality incidents detected by Sifflet are tracked and managed within your existing ServiceNow environment.
• Automated Incident Creation: Sifflet can now automatically create a ServiceNow incident whenever a monitor failure triggers a Sifflet incident. This eliminates manual steps and ensures rapid response to data quality problems.
• Customizable Incident Templates: Create custom ServiceNow incident templates within Sifflet and assign different templates to different Sifflet monitors. This allows you to tailor the information included in each ServiceNow incident, providing the right context for efficient resolution.
• Direct links from Sifflet to ServiceNow: Access your Sifflet-created ServiceNow incidents from Sifflet's UI.

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

App version: v458

You can now set a custom email subject when creating or editing monitors, instead of using the default Sifflet subject. This gives you more control over your email notifications, making it easier to prioritize and process alerts.

App version: v455

We've added a "dbt Runs" tab to all Sifflet assets generated from your dbt models (e.g., tables, views). This tab provides a centralized view of your dbt model execution history, allowing you to:

• Quickly see the status and duration of each run.
• Access the complete run history for the asset's dbt model.
• View useful metadata like every run's start and end time and its logs.

The tab will also be used to provide insights into the resource usage and efficiency of dbt runs. Additional information, such as estimated run costs, will be available soon!

App version: v453

When a new user is created in Sifflet, they will now receive an email with login instructions. The email guides them through setting a password or using SSO, based on the enabled login methods.

App version: v452

Two new APIs have been added:

• Decrypt Monitor Groups - Retrieve a dictionary for a specified Monitor of the hashed values alongside their decrypted values for a Monitor
• Decrypt Monitor Run Groups - Retrieve a dictionary for a specified Monitor Run of the hashed values alongside their decrypted Values

{
  "decryptedValues": {
    "7rhxMMfI/00nEPCeU4JF1yh6DG5HrDYpv6Ruq4npF/s=": [
      "Los Angeles"
    ],
    "RAEabd4cIdK7i6arZWQKoweEDcCUAFvkroRld/hyQzU=": [
      "New Orleans"
    ],
    "ZNMTMMDB9mVQeOOE4fQL3SBF1Q/U78hvgywLuASeZGI=": [
      "Philadelphia"
    ],
    "0ujO9ut/7CYQZWLVzYGUigbccqABrL0el0+GJv5xRTQ=": [
      "Brooklyn"
    ],
    "7kC4JpawlbYAk3gVWN9JOO+toV/lOnRgc0a2YWPD1wU=": [
      "San Francisco"
    ],
    "nXVWkm7SsR9kv4y8JL4zkY1Fo/KTlm42VE//lrdrt0A=": [
      "Chicago"
    ]
  }
}

Why is this useful? As these decrypted values reflect column values in the source database they are not stored or exposed everywhere in sifflet for compliance reasons, specifically not in the data sharing. These APIs allow you to map the hashed values in your data share with their true values.