Introducing Metaplane's end-to-end dbt observability: Full visibility before, during, and after deployment

We've all felt that moment of hesitation before deploying a change to our data pipelines. You’ve carefully made updates to a dbt model, reviewed the code, and everything checks out—but you still have a nagging feeling that something could go wrong. 

Will a critical dashboard stop updating, or will data quality issues surface in production?

What if you’re running dbt locally with dbt core, have you ever wondered what specific model is impacting the increase in runtime? Or cost? 

We know the feeling. That's why we're thrilled to introduce Metaplane’s end-to-end dbt observability features. With full visibility before, during, and after deployment, Metaplane’s end-to-end dbt observability helps you ship faster with fewer bugs, save costs, and fix issues immediately (when they do arise).

Before deployment: Prevent breaking changes in pull requests

What’s better than putting out fire? Not starting one. Which is why Metaplane's GitHub integration proactively looks at your dbt code changes to run regression tests and identify potential impacts on your data warehouse tables and BI dashboards. By providing data impact previews and data test previews directly within your pull requests, you can:

• Ship updates faster without compromising on data quality
• Reduce the risk of creating new issues while updating models
• Perform root cause analysis faster

"Metaplane's Github application has been indispensable as a tool for additional validation as we change code in our dbt instance. It makes it super simple to understand the downstream impact of any sort of change, and when paired with lineage, we can go directly to analysts to tell them 'X change has Y impact' to prevent accidental issues" - Rebecca Chapin, Senior Analytics Engineering Manager at Upright

During deployment: Real-time model runtime monitoring and alerting

Even with thorough testing, unexpected issues can still arise once your code is live. That's where Metaplane's machine learning-based runtime monitoring comes into play. Monitoring dbt job durations is useful as a leading indicator of potential data quality issues, particularly in cases where you have latency dependencies or a need for up-to-date data (i.e. real-time or near real-time).

• dbt Job Duration Monitoring: Metaplane tracks the runtime of your dbt jobs and alerts you when they take longer than usual. This can be an early indicator of issues like increased data volume, inefficient queries, or resource bottlenecks.
• Model-level visibility: Dive deep into individual models within your dbt jobs to pinpoint exactly where delays or failures are occurring. Understanding which models are causing slowdowns allows you to address the root cause quickly.
• Historical trends analysis: Access trends over time for the queries that power each model. Identify patterns such as increasing runtimes or frequent test failures, so you can proactively optimize performance.

By monitoring your dbt models during deployment, you ensure that your data pipelines remain healthy and performant. This means delivering reliable data to your stakeholders without interruption and maintaining the trust you've worked hard to build. You can also save yourself from unexpected spikes in cost.

"I love the dbt run time monitoring which helps us understand performance and spend. To have that history there and go back, we can see the improvements we made as a team over time." - Adam Smith, Analytics Manager at Imperfect Foods

After deployment: Faster issue resolution

Even with the best planning and real-time monitoring, certain issues only surface after deployment—issues that traditional CI/CD pipelines or runtime monitoring might miss entirely. Metaplane bridges this gap by providing immediate, detailed alerts when a dbt job fails due to a model error or a test failure. 

These alerts aren't just generic notifications—they include rich context such as specific dbt job run details, affected models, and relevant logs. For dbt Core users, this means not having to rely on orchestration tools like Airflow. And for dbt Cloud users, Metplane alerts provide all the necessary information to take action—without the need to log into the dbt web app.

• Catch issues before they escalate: Receive detailed notifications about errors and anomalies to Slack or Teams, allowing you to address problems before they impact downstream processes or end-users.
• Pinpoint root causes in record time: Metaplane's anomaly detection algorithms continuously monitor your data models for unusual patterns or deviations from the norm. Quickly trace issues back to their source, whether it's a recent pull request, an upstream data change, or an unexpected anomaly in your data models.
• Optimize performance and costs: Gain deep insights into query patterns and resource usage, enabling you to fine-tune your data warehouse for better performance and cost-savings.

By leveraging Metaplane's post-deployment capabilities, you're not just fixing issues—you're continuously improving your entire data ecosystem. This proactive approach means fewer fire drills, more reliable data, and ultimately, greater trust in your data products across your organization."Metaplane has made it so much faster not just to find issues but also who else to alert. We can see all of the models or data tests that are failing, how they're linked to each other, and see the reporting that's linked to all of that. We're a lot quicker now to communicate them to leadership and other business functions, and that's created more trust in the data and our team by extension." - Ashley Melanson, Lead Analytics Engineer at Dribbble

‍Put out fires before they start

With Metaplane's end-to-end dbt observability, you're no longer navigating your data landscape in the dark. From pre-deployment impact analysis to real-time monitoring and post-deployment insights, you have the tools to confidently manage your dbt workflow at every stage. This approach doesn't just solve problems—it prevents them and improves the quality and reliability of your entire data pipeline.