Most data pipelines break silently, leaving you with stale data and broken dashboards. You wake up Monday morning, check your analytics dashboard, and realize the numbers haven’t updated since Friday. Your team is asking questions about weekend performance, but you have no answers because your pipeline failed silently over the weekend.
When your pipelines use Airflow (orchestration), dbt (transformation), Snowflake (warehouse), and BigQuery (warehouse), you can build reliable pipelines that handle failures gracefully and recover automatically. These tools work together to create a robust data infrastructure that your team can trust.
The 5 pipeline reliability principles that matter
1. Error Handling
Effective error handling starts with catching errors early through validation, before they cascade through your pipeline. In Airflow, you can use sensors to check if source data exists before processing begins. With dbt, you can add data quality tests that validate incoming data before transformations run. When errors do occur, handle them gracefully by implementing retry logic for transient failures—like network timeouts or temporary API rate limits—and skip logic for non-critical issues that shouldn’t stop your entire pipeline.
For example, if your pipeline pulls data from an API and one endpoint is temporarily down, retry logic can automatically attempt the request again after a short delay. If a non-critical data source fails—like a marketing attribution feed that’s nice to have but not essential—skip logic allows the pipeline to continue processing other sources. Most importantly, alert your team immediately when errors happen so they can respond quickly. Set up Slack or email alerts in Airflow that notify your team when a pipeline fails, including context about what went wrong and where.
2. Idempotency
Idempotency ensures you can rerun pipelines safely without creating duplicates. This means your pipeline checks whether data already exists before inserting it. In Snowflake and BigQuery, you can use merge statements that update existing records or insert new ones based on unique keys. When failures occur, checkpointing allows you to resume from the point of failure rather than starting over. Store checkpoint information—like the last processed timestamp or record ID—in a metadata table that your pipeline can query on restart.
For partial failures, implement rollback mechanisms that can undo changes and restore your data to a consistent state. If your dbt transformation fails halfway through, use transactions to roll back all changes made during that run. In Airflow, you can configure tasks to clean up partial results if downstream tasks fail, ensuring your data warehouse never contains incomplete or inconsistent data.
3. Monitoring
Track your pipeline health by monitoring success rates over time. In Airflow, you can use the built-in monitoring dashboard to track task success rates, or integrate with tools like Datadog or Prometheus for more advanced metrics. Watch performance metrics like duration and throughput to identify bottlenecks before they become problems. If a dbt model that normally takes 5 minutes suddenly takes 30 minutes, that’s a signal that something has changed—maybe your data volume increased, or a transformation became inefficient.
Set up alerts that notify you when failures occur or when pipelines take longer than expected, giving you visibility into issues as they happen. Configure Airflow to send alerts when a DAG run exceeds its expected duration, or when a task fails more than a certain number of times. For Snowflake and BigQuery, monitor query performance and costs to catch inefficient transformations before they impact your budget.
4. Testing
Test your transformations with unit tests that validate individual logic components. In dbt, you can write custom tests that check specific business logic—like ensuring revenue calculations match expected formulas, or that date ranges are valid. Use integration tests to verify data quality and ensure transformations produce the expected outputs. Test that your dbt models produce the correct number of rows, that aggregations sum correctly, and that joins don’t create unexpected duplicates.
Finally, run end-to-end tests that validate the entire pipeline from source to destination, catching issues that unit tests might miss. Create test DAGs in Airflow that run your full pipeline with sample data, verifying that data flows correctly from source systems through transformations to your final tables. These tests should run automatically in your CI/CD pipeline before deploying changes to production.
5. Documentation
Document what each pipeline does, including its purpose and expected outputs. In Airflow, use DAG descriptions and task documentation to explain what each pipeline accomplishes. In dbt, use model descriptions and column descriptions to document what each transformation does and what the output represents. Document dependencies so others understand what data sources and services each pipeline requires—which APIs it calls, which database tables it reads from, and which downstream systems depend on its output.
Most importantly, document how to fix common failures so your team can resolve issues quickly without your intervention. Create runbooks that explain how to troubleshoot common errors—like “If you see ‘connection timeout’ errors, check the source API status page” or “If dbt tests fail, check the data quality report to see which columns have issues.” This documentation should be easily accessible, ideally in your Airflow UI or a shared wiki, so team members can find answers without interrupting you.
What to build first (week 1)
Start with a simple reliable pipeline that includes error handling to catch and handle errors gracefully. In Airflow, configure your DAGs with retry logic—set retries=3 and retry_delay=timedelta(minutes=5) on your tasks so they automatically retry transient failures. Add try-except blocks in your Python operators to catch and log errors before they crash the entire pipeline. In dbt, add data quality tests using dbt test to validate incoming data before transformations run.
Build in idempotency so you can rerun pipelines safely without creating duplicates. In Snowflake or BigQuery, use MERGE statements instead of INSERT statements. For example, instead of INSERT INTO customers SELECT * FROM staging_customers, use MERGE INTO customers USING staging_customers ON customers.id = staging_customers.id WHEN MATCHED THEN UPDATE SET ... WHEN NOT MATCHED THEN INSERT .... This ensures that running the pipeline multiple times produces the same result.
Add monitoring to track health and performance metrics over time. Set up Airflow’s built-in monitoring or integrate with Datadog to track DAG success rates, task durations, and failure counts. In dbt, use the dbt run command with logging enabled to track model execution times. Create a simple dashboard that shows pipeline health metrics—success rate, average duration, and recent failures.
Set up alerting to notify your team when failures occur. Configure Airflow to send Slack notifications when a DAG fails, including the DAG name, task that failed, and error message. Set up alerts in Snowflake or BigQuery when queries exceed cost thresholds or take longer than expected. Use PagerDuty or similar tools for critical pipelines that need immediate attention.
Finally, implement testing to validate your transformations work as expected. In dbt, write tests for your models using dbt test. Start with simple tests like checking for null values in required columns, then add more complex tests that validate business logic. Create a test DAG in Airflow that runs your full pipeline with sample data to catch integration issues.
Once you have these basics working, add retry logic for automatic retries on transient failures. Configure exponential backoff so retries don’t overwhelm failing systems—wait 1 minute before the first retry, 2 minutes before the second, 4 minutes before the third. Implement checkpointing so pipelines can resume from the point of failure rather than starting over. Store checkpoint data—like the last processed timestamp—in a metadata table that your pipeline queries on startup.
Add rollback capabilities to undo changes when failures occur. Use transactions in Snowflake and BigQuery to ensure atomicity—if any part of a transformation fails, roll back all changes. In dbt, use the --full-refresh flag carefully and consider using incremental models that can be safely rerun. And don’t forget documentation that explains how to maintain and troubleshoot each pipeline. Create runbooks in your wiki or Confluence that explain common failure scenarios and how to resolve them.
Why most data pipelines fail
Most data pipelines fail because errors aren’t handled properly, causing failures to crash entire pipelines instead of being caught and managed gracefully. I’ve seen pipelines that fail because one API endpoint is down, even though the pipeline processes data from ten different sources. Without proper error handling, a single failure cascades through the entire pipeline, leaving you with no data at all instead of partial data from the sources that are still working.
Reruns aren’t safe, which means running a pipeline twice creates duplicate data and inconsistent results. I’ve worked with teams that had to manually delete duplicate records every time someone accidentally triggered a pipeline rerun. Without idempotency, you can’t safely recover from failures—if a pipeline fails halfway through, you can’t just rerun it because you’ll get duplicate data for the records that were already processed.
Monitoring is often missing entirely, so teams don’t know when pipelines break until someone notices stale data. I’ve seen dashboards that showed data from three days ago because no one noticed the pipeline had been failing silently. Without monitoring, you’re flying blind—you have no idea if your pipelines are working until someone complains about missing data.
Testing is frequently absent, meaning transformations aren’t validated and bugs slip through to production. I’ve seen dbt models that produced incorrect aggregations because no one tested them with real data. Without testing, you’re deploying code changes blindly, hoping that your transformations work correctly but having no way to verify it.
When you build reliable pipelines, you can handle failures gracefully by implementing retry logic for transient issues and skip logic for non-critical problems. If an API is temporarily down, your pipeline retries automatically instead of failing immediately. If a non-critical data source fails, your pipeline continues processing other sources and alerts you about the issue.
You can rerun pipelines safely without creating duplicates thanks to idempotency checks. If a pipeline fails halfway through, you can simply rerun it and it will pick up where it left off, updating existing records and inserting new ones without creating duplicates. This gives you confidence to recover from failures quickly.
You can monitor pipeline health continuously and catch issues early before they impact downstream systems. With proper monitoring, you’ll know within minutes if a pipeline fails, not days later when someone notices stale data. You’ll see performance trends that help you identify bottlenecks before they become critical problems.
Most importantly, you can trust your transformations because they’ve been validated through comprehensive testing. When you deploy a new dbt model, you know it works correctly because your tests have verified it. When you make changes to a pipeline, you can run your test suite to ensure nothing broke.
The hidden cost of unreliable pipelines
When pipelines are unreliable, data becomes stale because pipelines break frequently and updates stop flowing. I’ve worked with teams where Monday morning meetings were spent discussing why the weekend data wasn’t available, instead of discussing business insights. When your sales dashboard shows data from last Thursday, your sales team can’t make informed decisions about which leads to prioritize today.
Dashboards break when they can’t find the data they need, leaving your team without visibility into key metrics. I’ve seen executives trying to review quarterly performance only to discover that the dashboard has been showing stale data for weeks. When your marketing team can’t see yesterday’s campaign performance, they can’t optimize today’s campaigns effectively.
Trust erodes as your team loses confidence in the data, questioning every number and making decisions based on gut feeling instead of facts. I’ve seen teams that stopped using dashboards entirely because they couldn’t trust the numbers. When your finance team questions every revenue number because pipelines have failed before, they’ll revert to manual spreadsheets instead of using your data infrastructure.
Time is wasted constantly debugging failures instead of focusing on building new features. I’ve seen data engineers spending 30% of their time debugging pipeline failures instead of building new capabilities. When you’re constantly putting out fires, you can’t focus on strategic work that moves the business forward.
Reliable pipelines mean your data stays fresh because pipelines run consistently and handle failures gracefully. When a transient failure occurs—like a temporary API outage—your pipeline retries automatically and completes successfully. Your team wakes up Monday morning knowing that weekend data is already available and ready to analyze.
Dashboards work reliably because data is always available when they need it. Your sales team can check real-time performance metrics at any time, confident that the numbers are up-to-date. Your marketing team can see yesterday’s campaign performance first thing in the morning, allowing them to optimize today’s campaigns based on fresh data.
Trust is high because your team knows they can count on the data to be accurate and up-to-date. When your finance team reviews quarterly numbers, they trust that the data reflects reality because pipelines have been running reliably for months. Your executives can make strategic decisions confidently, knowing that the data they’re seeing is accurate and current.
Time is saved because you’re not constantly debugging failures, freeing your team to focus on higher-value work. Instead of spending hours every week troubleshooting pipeline issues, your data engineers can focus on building new data products that drive business value. Your team can move faster because they’re not blocked by unreliable infrastructure.