Databricks to s3

[GOW]

I am new to data engineering in Databricks. I need some guidance surrounding Databricks to s3. Can I get an example job or approach to do this?

[Kaniz]

Hi @GOW, Let’s explore how you can create a Databricks job to move data from Databricks to Amazon S3.

Here’s an example approach:

1. Create a Databricks Job:

   • In your Databricks workspace, navigate to Workflows in the sidebar and click the “+” button to create a new job.
   • Provide a name for your job.
   • Choose the type of task you want to run (e.g., notebook, JAR, Python script).
   • Configure the cluster where the task will run (either a new job cluster or an existing all-purpose cluster).
   • Add any dependent libraries if needed.
   • Pass parameters to your task if required.
   • Set up email notifications for task start, success, or failure.

2. Write Data to Amazon S3:

   • Suppose you have a DataFrame (df) that you want to write to a CSV file in Amazon S3.
   • Use the following code snippet to write the DataFrame to a CSV file and pass the file path as an argument:

   df = (spark.read
         .format("csv")
         .option("inferSchema", True)
         .option("header", True)
         .option("sep", ",")
         .load("s3://<bucket_name>/<subfolder>/"))
   

   Replace <bucket_name> and <subfolder> with your actual S3 bucket and subfolder.

3. Run the Job:

   • Once your job is set up, you can run it manually or schedule it to run at specific intervals.
   • Monitor job runs using the Databricks Jobs UI.

Remember to adjust the specifics according to your use case, such as the data format, target S3 location, and any additional processing steps you need.

For more detailed information, refer to the official Databricks documentation on creating and running jobs¹.

Happy data engineering! 🚀

[GOW]

Thank you for the reply. Can I apply this to dbt or using a dbt macro to unload the data? So dbt models running in Databricks?