Apache Polaris: The Definitive Guide

Chapter 7. Using Apache Polaris with Apache Spark

With your experimental Apache Polaris environment successfully set up on your laptop, you’re now ready to start exploring its integration with Apache Spark. If you’ve followed the previous chapter, you should have your environment running and be able to access Jupyter Notebook at http://localhost:8888. While we will be working in this local setup, the steps and concepts covered in this chapter are equally applicable to any Spark environment, whether it’s a local cluster or a cloud-based setup.

Apache Spark is a powerful, open source, unified analytics engine for processing large-scale data. Its in-memory computation and distributed architecture make it incredibly fast and efficient for handling complex workloads, from batch processing to real-time analytics and machine learning tasks.

In this chapter, we’ll dive into the practical steps to connect your Polaris catalog to Spark, explore the Spark DataFrame API, execute SQL queries on Polaris-managed data, and even use Spark Streaming to interact with Polaris in real-time. By the end of this chapter, you’ll have a comprehensive understanding of how to harness the combined power of Apache Polaris and Apache Spark in your data workflows. Let’s get started!

You can see all these code snippets as well in the book’s GitHub repository:

• https://github.com/developer-advocacy-dremio/apache-polaris-the-definitive-guide.

Connecting Your Apache Polaris Catalog to Apache Spark

To use Apache ...