You can use a Databricks job to orchestrate your data processing, machine learning, or data analytics pipelines on the Databricks platform. Databricks Jobs support a number of workload types, including notebooks, scripts, Delta Live Tables pipelines, Databricks SQL queries, and dbt projects. The following articles guide you in using the features and options of Databricks Jobs to implement your data pipelines.
dbt task type if you are doing data transformation with a dbt core project and want to integrate that project into a Databricks job, or you want to create new dbt transformations and run those transformations in a job. See Use dbt transformations in a Databricks job.
Python wheels are a standard way to package and distribute the files required to run a Python application. You can easily create a job that uses Python code packaged as a Python wheel with the
Python wheel task type. See Use a Python wheel in a Databricks job.
Libraries and applications implemented in a JVM language such as Java and Scala are commonly packaged in a Java archive (JAR) file. Databricks Jobs supports code packaged in a JAR with the
JAR task type. See Use a JAR in a Databricks job.
A common way to manage version control and collaboration for production artifacts is to use a central repository such as GitHub. Databricks Jobs supports creating and running jobs using notebooks or Python code imported from a repository, including GitHub or Databricks Repos. See Use version-controlled source code in a Databricks job.
Databricks recommends using Databricks Jobs to orchestrate your workflows. However, Apache Airflow is commonly used as a workflow orchestration system and provides native support for Databricks Jobs. While Databricks Jobs provides a visual UI to create your workflows, Airflow uses Python files to define and deploy your data pipelines. For an example of creating and running a job with Airflow, see Orchestrate Databricks jobs with Apache Airflow.