June 2025
Beginner to intermediate
438 pages
9h 17m
English
Content preview from Dataproc Cookbook
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Chapter 13. Using Spark Notebooks on Dataproc
Interactive development tools like notebooks help data engineers easily collaborate as well as explore and analyze data, thereby improving developer productivity. Choosing the right notebook environment for developing and running Spark applications on GCP is essential to optimizing workflows and leveraging GCP’s capabilities.
This chapter explores the various notebook environments available on GCP and provides guidance on selecting the most suitable one for your Spark workloads. We will demonstrate how to set up a Jupyter Notebook on Dataproc on GCE clusters and run different Spark workloads, such as Spark Scala and PySpark, using various notebook kernels.
We will also cover advanced topics, including managing libraries, configuring Spark properties, and working with Dataproc-enabled Vertex AI Workbench instances. By the end of this chapter, you’ll have a comprehensive understanding of how to efficiently configure, execute, and manage Spark jobs using notebooks on GCP’s Dataproc service.
Deciding Which Notebook Environments to Choose
Problem
There are various notebook environments in GCP, including Jupyter, JupyterLab, Apache Zeppelin, and Colab. Which one should you choose for running Spark applications?
Solution
Use Dataproc-enabled Vertex AI Workbench (“Creating Dataproc-Enabled Vertex AI Workbench Instances”) for developing and running Spark applications on GCP. It provides a managed JupyterLab Notebook environment to develop ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.