Building a notebook pipeline with precedence constraints

In Azure Databricks, data pipelines are built using Workflows (Jobs), where notebooks are added as Tasks that execute your ETL/ELT logic. Precedence constraints define the execution order and dependency logic between tasks, allowing you to control which tasks run sequentially or conditionally based on the outcome of prior tasks.

1 · Learn the must-know

• 1A Job can contain multiple Tasks, and each Task can run a notebook, JAR, Python script, SQL file, or dbt task, with notebooks being the most common for data engineering pipelines.
• 2Task dependencies are configured via the 'Depends on' setting in the Jobs UI, which creates a directed acyclic graph (DAG) determining execution order.
• 3Run conditions (precedence constraints) can be set to control when a downstream task runs, such as 'All succeeded', 'At least one succeeded', 'None failed', 'All done', or 'At least one failed'.
• 4Parameters and values can be passed between dependent notebook tasks using task values (dbutils.jobs.taskValues.set/get), enabling dynamic pipeline behavior based on upstream results.
• 5If a task fails and no alternate run condition is configured, all downstream tasks that depend on it are skipped by default, halting that branch of the pipeline.
• 6Each notebook task can specify its own cluster (job cluster or existing all-purpose cluster) and can be configured independently, allowing different tasks in the same pipeline to use different compute resources.

2 · Check your understanding