This month we focused on making Elementary faster, more reliable, and giving you new ways to monitor and understand your data. From UI improvements to deeper lineage and anomaly detection, here’s what’s new:
📊 Dimension Anomalies in the UI
Dimension-level anomaly detection just got a big upgrade:
- Visualized directly in the UI
- Interactive graphs – choose which dimension you want to explore
This helps you spot unusual behavior in key segments (like country, product line, or campaign) before it impacts downstream reporting.
🔗 Hex Integration
We’re excited to announce Hex integration!
Elementary now extends column-level lineage all the way into Hex notebooks and dashboards. This means:
- Full visibility into downstream dependencies
- Easier impact analysis when a data issue is detected
- Greater trust in your analytics and experimentation
With this, you can trace reliability from source to warehouse to the actual notebook or dashboard where it’s consumed.
Learn more and set it up here https://docs.elementary-data.com/cloud/integrations/bi/hex
🧩 Struct Columns Support
By customer request, we’ve added support for struct columns and their descriptions.
- Automatically parsed and added to the catalog
- Displayed as
column_name.field_namefor clarity - Works seamlessly with lineage, health scores, and descriptions
This gives you much better visibility into nested schemas without extra setup.
🔔 Sync & Alert Audit Logs
We’ve expanded Elementary’s audit capabilities to include Syncs and Alerts — so you’re never in the dark about what happened and when.
- Sync Logs – track when a sync ran, how long it took, and whether it succeeded. This makes it easy to monitor performance and know exactly when data was pushed.
- Alert Logs – see exactly when alerts were sent after the sync completed, and how long it took until all alerts went out.
Together, these logs give you a clear picture of how your syncs and alerts are running.
🤖 Automated Monitors – Rivery Support
We’ve expanded Automated Freshness and Volume Monitors to support Rivery.
Rivery is a data integration platform that helps teams move and transform data from hundreds of sources — like Salesforce, Google Ads, and Shopify — into their warehouse.
With this update, you can:
- Automatically monitor pipelines created in Rivery
- Detect anomalies and failures without manual setup, such as late ingestions or unexpected changes in row counts
This ensures your ingestion pipelines are covered from the moment they’re created.
⚡ Performance Enhancements
Elementary is now faster and more reliable across the board. We rolled out major improvements to the Test Overview page and related dashboards, so you’ll notice:
- Smoother navigation, even with large projects
- Faster load times when viewing test results
- Increased reliability in rendering and drill-downs
This means less waiting and more confidence that what you see in the UI reflects the true state of your pipelines.
✍️ Renaming dbt Tests (Bug Fix)
We heard your feedback! You can now rename all dbt tests, including those from the dbt package.
Simply add a name: property at the top of your dbt test in the YAML file, and Elementary will display the custom name everywhere in the UI. This small fix makes managing and triaging tests much easier.
🛠️ dbt Fusion Update
We’re continuing to work closely with the dbt team to close the final gaps in Fusion support. Our first Beta version will be released soon! 🎉
Most workflows will run smoothly, though some minor compatibility issues may remain in the early release. We look forward to rolling this out and incorporating feedback from early adopters.
🎥 MCP Server Webinar Recap
Last week we hosted a session about the Elementary MCP server, where we walked through engineering and data consumption use cases.
For engineering teams, MCP helps with:
- Preventing data incidents before they hit the business
- Shortening MTTR with clearer root cause context
- Avoiding blind spots in detection coverage
- Reducing wasted costs and improving performance
For data consumers, MCP drives:
- Increased data usage and adoption across the org
- Building trust with stakeholders through visibility into reliability
If you missed it, you can watch the full webinar here. Setup in minutes and try it out in your own environment!
This month we focused on making Elementary faster, more reliable, and giving you new ways to monitor and understand your data. From UI improvements to deeper lineage and anomaly detection, here’s what’s new:
📊 Dimension Anomalies in the UI
Dimension-level anomaly detection just got a big upgrade:
- Visualized directly in the UI
- Interactive graphs – choose which dimension you want to explore
This helps you spot unusual behavior in key segments (like country, product line, or campaign) before it impacts downstream reporting.
🔗 Hex Integration
We’re excited to announce Hex integration!
Elementary now extends column-level lineage all the way into Hex notebooks and dashboards. This means:
- Full visibility into downstream dependencies
- Easier impact analysis when a data issue is detected
- Greater trust in your analytics and experimentation
With this, you can trace reliability from source to warehouse to the actual notebook or dashboard where it’s consumed.
Learn more and set it up here https://docs.elementary-data.com/cloud/integrations/bi/hex
🧩 Struct Columns Support
By customer request, we’ve added support for struct columns and their descriptions.
- Automatically parsed and added to the catalog
- Displayed as
column_name.field_namefor clarity - Works seamlessly with lineage, health scores, and descriptions
This gives you much better visibility into nested schemas without extra setup.
🔔 Sync & Alert Audit Logs
We’ve expanded Elementary’s audit capabilities to include Syncs and Alerts — so you’re never in the dark about what happened and when.
- Sync Logs – track when a sync ran, how long it took, and whether it succeeded. This makes it easy to monitor performance and know exactly when data was pushed.
- Alert Logs – see exactly when alerts were sent after the sync completed, and how long it took until all alerts went out.
Together, these logs give you a clear picture of how your syncs and alerts are running.
🤖 Automated Monitors – Rivery Support
We’ve expanded Automated Freshness and Volume Monitors to support Rivery.
Rivery is a data integration platform that helps teams move and transform data from hundreds of sources — like Salesforce, Google Ads, and Shopify — into their warehouse.
With this update, you can:
- Automatically monitor pipelines created in Rivery
- Detect anomalies and failures without manual setup, such as late ingestions or unexpected changes in row counts
This ensures your ingestion pipelines are covered from the moment they’re created.
⚡ Performance Enhancements
Elementary is now faster and more reliable across the board. We rolled out major improvements to the Test Overview page and related dashboards, so you’ll notice:
- Smoother navigation, even with large projects
- Faster load times when viewing test results
- Increased reliability in rendering and drill-downs
This means less waiting and more confidence that what you see in the UI reflects the true state of your pipelines.
✍️ Renaming dbt Tests (Bug Fix)
We heard your feedback! You can now rename all dbt tests, including those from the dbt package.
Simply add a name: property at the top of your dbt test in the YAML file, and Elementary will display the custom name everywhere in the UI. This small fix makes managing and triaging tests much easier.
🛠️ dbt Fusion Update
We’re continuing to work closely with the dbt team to close the final gaps in Fusion support. Our first Beta version will be released soon! 🎉
Most workflows will run smoothly, though some minor compatibility issues may remain in the early release. We look forward to rolling this out and incorporating feedback from early adopters.
🎥 MCP Server Webinar Recap
Last week we hosted a session about the Elementary MCP server, where we walked through engineering and data consumption use cases.
For engineering teams, MCP helps with:
- Preventing data incidents before they hit the business
- Shortening MTTR with clearer root cause context
- Avoiding blind spots in detection coverage
- Reducing wasted costs and improving performance
For data consumers, MCP drives:
- Increased data usage and adoption across the org
- Building trust with stakeholders through visibility into reliability
If you missed it, you can watch the full webinar here. Setup in minutes and try it out in your own environment!
