How Control Plane and Data Plane Interact in Databricks

Let’s take a real-world ETL workflow in Databricks on AWS to see how the Control Plane and Data Plane work together.

Scenario: Data Processing Pipeline

You are a Data Engineer at an e-commerce company. Your task is to process customer orders from an S3 bucket, perform data transformations, and store the results in a Delta Table.

Workflow: Steps in Databricks

1. You log in to Databricks and open a notebook.

2. You create a cluster to process data.

3. You submit a PySpark job that reads data from Amazon S3, cleans it, and writes the output to a Delta Table in AWS Glue Catalog.

4. Databricks executes the job and saves the results in the customer’s AWS account.

5. Job completion details are logged in the Databricks workspace.

How Control Plane and Data Plane Work in Each Step

Visualization of Control Plane vs. Data Plane in This Scenario

Key Takeaways

• Security: Your raw data never leaves your AWS account because Databricks only manages job orchestration.

• Scalability: Databricks spins up EC2 instances dynamically, scaling up/down as needed.

• Efficiency: The separation allows Databricks to manage metadata while keeping heavy data workloads inside your cloud.

• Integration: The processed data can be queried via Databricks SQL, stored in Delta Lake, or moved to another system (e.g., Redshift, Snowflake).