Dataproc Cookbook

Chapter 2. Running Hive/Spark/Sqoop Workloads

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In the world of big data processing and analysis, Google Cloud’s Dataproc simplifies managing and executing large-scale data workloads. In this chapter, we will cover the essential steps for running various big data jobs on your Dataproc cluster. A “job” in this context represents a specific task or workload to be executed on the Dataproc cluster. This can be a Hive query for structured data processing, a Spark application for distributed computation, or a Sqoop data transfer for moving data between databases and Hadoop.

Prerequisites: To effectively follow along with this chapter, you will need:

Dataproc API: Ensure that the Dataproc API is enabled for your project. This API is essential for interacting with your cluster.

Existing Dataproc Cluster: You will need a Dataproc cluster already created and running on the Google Cloud Platform. If you haven’t set one up yet, Chapter 1 provides guidance on cluster creation.

We will explore the different ...