data monitoring

Learn all about source-to-target monitors—a new way to ensure that your data stays consistent between your data sources and your target destination.

Get the rundown on automated data quality checks—including what they are, what to monitor, and how to set them up.

Extend observability to your ETL tools by monitoring individual connectors for better control and insights.

Adaptive flatline alerts balance detection and false alarms. Learn how to set up timely notifications for data ingestion patterns.

Set the time zone at both account and monitor levels for enhanced control over your Metaplane experience.

Learn more about how Metaplane's new dashboard makes it easier than ever to manage data quality from one place.

Bulk actions in Metaplane let you reset predictions, run monitors manually, and update frequencies for seamless data oversight.

The new Metaplane monitor platform redesigns alerting with greater visibility, better predictability, and streamlined data quality tools.

Learn more about how data quality tests, data monitoring, and data observability differ, while still improving data quality in their own ways.

Data observability is crucial for modern data teams. This blog defines it, distinguishes it from similar concepts, and highlights its importance.

Metaplane users can now add tags to their tables in Metaplane, including an integration with dbt tags and incident workflow functionality.

Discover how dbt helps data teams transform data and run quality checks to ensure accuracy, reliability, and trust in data-driven decision-making.

Monitor data segments with WHERE clauses in Metaplane, ideal for adapting to business changes or focusing on specific areas.

Manage your Metaplane monitors directly in dbt to streamline workflows and improve configuration efficiency.

We've redesigned incidents from the ground up to help you identify the root cause with confidence and communicate with stakeholders with ease.

Metaplane now provides helpful suggestions to help you ensure that all your most important tables and columns are monitored.

Metaplane has three new out-of-the-box monitor types for numeric columns: SUM, percent negative, and percent zero.

Metaplane has built a new way to manage your monitors, including improved filtering, bulk actions, and increased visibility into the status of your monitors.

Easily monitor specific data segments with Metaplane's new partition monitors to identify root causes of data quality issues in seconds.

Automate monitoring in Metaplane with schema settings. Add row counts and freshness tests for every new table in your critical schema.

Great Expectations is for catching known issues via binary tests on critical tables, while Metaplane identifies unknown issues with scalar criteria.

dbt tests catch known issues with binary criteria, while Metaplane detects unknown issues using scalar criteria across various assets.

You can now see the number of open incidents on schemas, tables, and columns throughout the web application

Incidents help group together related failing tests so data teams can be the first to know about data quality issues.

Add Metaplane tests directly from table and column pages for faster, more integrated quality checks.

Granular alert routing in Metaplane simplifies monitoring with custom rules for destination alerts and streamlined issue detection.

Slack alerts now have a “Re-run test” button so you can manually check if an alert has resolved or if it’s still an ongoing issue.

Reset predictions for all tests in one section of your warehouse with a single click for quicker adjustments.

You can now apply bulk actions within the context of an entity page such as muting, changing the schedule, or disabling all associated tests.

Metaplane's Snowflake app offers secure, native data observability with ease-of-adoption, changing how software integrates with data.

Monitor Snowflake table+column usage with Metaplane to prioritize data issues and understand critical assets in your warehouse.