Building a scalable, automated data pipeline using Spark, Kubernetes, GCS, and Airflow allows data teams to efficiently process and orchestrate large data workflows in cloud-native environments. This…

Data Engineer Things

This guide provides step-by-step instructions for setting up a scalable, automated data pipeline using Spark on Kubernetes with Google Cloud Storage (GCS) integration, managed by Apache Airflow. It includes configuring custom Docker images for Spark with GCS support, installing and configuring the Spark Operator and Airflow, creating a custom Airflow operator for submitting Spark jobs, and setting up necessary role-based access controls. By the end, readers will have a robust, cloud-native data engineering platform capable of handling complex data workflows efficiently.

Cloud-Native Data Engineering: Orchestrating Spark on Kubernetes with Custom Airflow Operator and GCS Integration

2. Create the Spark Application Image with GCS Integration

4. Configure Kind to Pull the Local Docker Image

5. Install the Spark Operator Using the Custom Image