Introduction
In today’s data-driven landscape, efficient data management and automation are paramount. A seamless solution for organizing and automating data processing processes is provided by the combination of Databricks, a potent big data ecosystem and Apache Airflow, a sophisticated workflow orchestration platform.
Automating Databricks Job Execution with Apache Airflow
Data engineers can easily schedule, monitor, and execute tasks by integrating Databricks with Airflow, assuring the reliable and efficient operation of data pipelines. This blog post will provide you a step-by-step tutorial on how to set up and automate Databricks job execution using Apache Airflow, helping you to optimize your data workflows.
key Points
- Introduction to Databricks and Apache Airflow.
- Benefits of automating Databricks jobs with Airflow.
- Setting up the environment: prerequisites and configurations.
- Creating an Airflow DAG to trigger Databricks jobs.
- Detailed walkthrough of the DAG code.
- Monitoring and troubleshooting the workflow.
- Best practices and tips for efficient job automation.
- Conclusion and future enhancements.
Technology Used
Apache Airflow, Databricks
Benefits of Automating Databricks Jobs with Airflow
- Enhanced Scheduling and Orchestration:
- Airflow’s robust scheduling capabilities enable automated Databricks job executions at predefined times or intervals, ensuring consistent and timely data processing without manual intervention.
- Airflow’s robust scheduling capabilities enable automated Databricks job executions at predefined times or intervals, ensuring consistent and timely data processing without manual intervention.
- Improved Error Handling and Recovery
- Airflow’s built-in error handling and retry mechanisms effectively manage failures, ensuring failed Databricks jobs can be automatically retried or flagged for prompt resolution.
- Scalability and Flexibility:
- Airflow’s adaptability allows you to easily scale your workflows as your data processing needs evolve, accommodating new tasks, modifications, and complex workflows involving multiple Databricks jobs and other services.
- Airflow’s adaptability allows you to easily scale your workflows as your data processing needs evolve, accommodating new tasks, modifications, and complex workflows involving multiple Databricks jobs and other services.
- Centralized Workflow Management:
- Airflow provides a centralized platform for managing all your data workflows, offering a unified interface to monitor, manage, and optimize Databricks job execution and related tasks.
- Airflow provides a centralized platform for managing all your data workflows, offering a unified interface to monitor, manage, and optimize Databricks job execution and related tasks.
- Extensive Monitoring and Logging:
- Airflow’s robust monitoring and logging features offer detailed insights into Databricks job execution, aiding in identifying bottlenecks, tracking performance, and maintaining pipeline health.
- Airflow’s robust monitoring and logging features offer detailed insights into Databricks job execution, aiding in identifying bottlenecks, tracking performance, and maintaining pipeline health.
- Seamless Integration with Other Tools:
- Airflow’s compatibility with a wide range of data processing and storage tools enables the creation of comprehensive workflows that not only trigger Databricks jobs but also interact with databases, cloud storage, and messaging services.
- Airflow’s compatibility with a wide range of data processing and storage tools enables the creation of comprehensive workflows that not only trigger Databricks jobs but also interact with databases, cloud storage, and messaging services.
- Cost Efficiency:
- Automation reduces the need for manual oversight, freeing up valuable time and resources. This leads to cost savings and allows your team to focus on more strategic initiatives.
- Automation reduces the need for manual oversight, freeing up valuable time and resources. This leads to cost savings and allows your team to focus on more strategic initiatives.
- Consistent and Reliable Data Pipelines:
- Automation ensures consistent and reliable data processing, minimizing human error and guaranteeing smooth pipeline execution, delivering accurate and timely data for analysis.
Top level workflow
Fig 1: Triggering Databricks Job using Airflow
Setting Up the Environment?
- Prerequisites
- Databricks Account and Workspace: Ensure you have access to a Databricks workspace. If you don’t have one, you can sign up for a Databricks account and create a workspace. Contact with the account administrator.
- Apache Airflow Installed: Airflow should be installed and running in your environment.
- Databricks API Token: Generate a Databricks API token(PAS → Personal access token) to authenticate API requests. This token will be used to configure the connection between Airflow and Databricks. You can’t generate PAS without proper permission.
- Configurations
- Installing Necessary Airflow Providers and Packages
You need to install the airflow databricks provider package to enable Airflow to interact with Databricks.
- pip install apache-airflow-providers-databricks
- Installing Necessary Airflow Providers and Packages
- Prerequisites
- Setting Up Databricks Credentials in Airflow
- Accessing Airflow UI
- Open your Airflow UI in a web browser.
- Creating a Databricks Connection
- Admin → Connections
- Click + to create new connections
- Fill in the connection details as follows:
- Connection id: any name
- Connection type: HTTP
- Host: https://dbc-136d71c6.cloud.databricks.com
- Password: Databricks account PAS(personal account token)
- Creating a Databricks Job
- Login to Databricks account
- Create a notebook and add some code
- Navigate to Workflow from left sidebar
- Click on create job
- Rename the job
- Fill in the config details as follows:
- Task name
- Type
- Source
- Path
- Cluster
- Notifications: By default is EmaiL
- Done
- Test by clicking on Run On button from top right corner
- Accessing Airflow UI
from airflow import DAG
from datetime import datetime, timedelta
from airflow.operators.bash import BashOperator
from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator
# dag config default_args
default_args = {
'owner' : 'admin',
'retries': 2,
'retry_delay': timedelta(seconds=10)
}
with DAG(dag_id='RunDatabricksJob'
, dag_display_name="Run the Databrciks Job by Airflow"
, default_args=default_args
, description='Trigger Databrciks job by airflow'
, start_date=datetime(2024, 9, 9)
, schedule_interval=None # manual trigger
, catchup=False):
# upstream task
task1 = BashOperator(task_id="task1",
bash_command="sleep 1")
# downstream task
task2 = DatabricksRunNowOperator(task_id="task2",
databricks_conn_id="databrciks_conn",
job_id="503002250194325",)
task1 >> task2
Conclusion
Integrating Apache Airflow with Databricks to automate job executions offers substantial advantages for data engineering workflows. This powerful combination not only enhances scheduling and orchestration but also ensures robust error handling, scalability, and centralized management of data tasks.
By following the steps outlined in this guide, you can create a seamless workflow that triggers Databricks jobs using Airflow, optimizing your data processing pipelines for efficiency and reliability.
Automating these processes empowers data engineers to focus on more strategic tasks, improving productivity and ensuring that data pipelines run consistently and accurately.
As you gain experience, consider exploring further enhancements such as parameterizing Databricks jobs, integrating with additional services, or incorporating more complex workflows. The synergy between Airflow and Databricks provides a robust platform for managing and automating your data workflows, paving the way for more efficient and effective data operations.
By implementing these practices, you’ll be well-equipped to meet the demands of modern data engineering, ensuring your data infrastructure is both robust and adaptable to future needs. With Vivasoft as your partner, you can enhance your processes even further. Happy automating!
