DBT : Best Practices for Restartable dbt Jobs: Tips for Resilient Data Pipelines


To ensure restartability in dbt jobs, you can use a combination of incremental models, snapshots, and custom materializations. Additionally, it’s important to leverage the power of dbt’s built-in features and good orchestration practices. Here are a few suggestions to help you schedule dbt jobs for restartability:

1. Incremental models: Configure your dbt models to run incrementally. Incremental models only process new or updated records since the last successful run, which can help resume a job after a failure. To configure a model as incremental, set the materialization in the model’s config block:

        materialized = 'incremental'

Also, add a condition in the model SQL to filter out previously processed records based on a unique key and timestamp.

2. Snapshots: Use snapshots to capture the history of your source data. Snapshots store a point-in-time version of your data, allowing you to track changes over time. This can help you identify and recover from any issues that may arise.

3. Custom materializations: If the built-in materializations are not sufficient for your use case, you can create custom materializations that better suit your needs. Custom materializations can help you better control the behavior of your models during execution and recovery from failures.

4. Use dbt’s –state flag: The –state flag allows you to compare your current run with a previous run, enabling dbt to only process the models that have changed. This can help you save time and resources when restarting a job after a failure. To use the –state flag, run:

dbt run --state <path_to_previous_manifest>

5. Implement retries and backoff strategies: When using an orchestration tool (e.g., Apache Airflow, Dagster, or Prefect) to schedule and manage your dbt jobs, configure the tool to implement retries and backoff strategies in case of failures. This will help you automatically restart the failed job after a specific time, allowing for a more resilient workflow.

6. Modularize your project: Break your dbt project into smaller, more manageable units to improve restartability. This can be done by creating separate models for each stage in your data transformation pipeline, and then using ref() and source() functions to reference these models.

7. Use event-based triggers: Configure event-based triggers in your orchestration tool to only run specific jobs when there’s new or changed data. This can help minimize the impact of failures and improve the overall resilience of your data pipeline.

By implementing these practices, you can make your dbt jobs more restartable, enabling you to recover from failures more efficiently and maintain a resilient data pipeline.

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