Wow! eBook
Dataproc Cookbook:Running Spark and Hadoop Workloads in Google Cloud
by: Narasimha Sadineni (Author), Anuyogam Venkataraman (Author)
Publisher: O'Reilly Media
Publication Date: 2025-07-08
Language: English
Print Length: 436 pages
ISBN-10: 1098157702
ISBN-13: 9781098157708
Book Description
Want to build big data solutions in Google Cloud? Dataproc Cookbook is your hands-on guide to mastering Dataproc and the essential GCP fundamentals--like networking, security, monitoring, and cost optimization--that apply across Google Cloud services. Learn practical skills that not only fast-track your Dataproc expertise, but also help you succeed with a wide range of GCP technologies. Written by data experts Narasimha Sadineni and Anu Venkataraman, this cookbook tackles real-world use cases like serverless Spark jobs, Kubernetes-native deployments, and cost-optimized data lake workflows. You'll learn how to create ephemeral and persistent Dataproc clusters, run secure data science workloads, implement monitoring solutions, and plan effective migration and optimization strategies. Create Dataproc clusters on Compute Engine and Kubernetes Engine Run data science workloads on Dataproc Execute Spark jobs on Dataproc Serverless Optimize Dataproc clusters to be cost effective and performant Monitor Spark jobs in various ways Orchestrate various workloads and activities Use different methods for migrating data and workloads from existing Hadoop clusters to Dataproc
Editorial Reviews
Want to build big data solutions in Google Cloud? Dataproc Cookbook is your hands-on guide to mastering Dataproc and the essential GCP fundamentals--like networking, security, monitoring, and cost optimization--that apply across Google Cloud services. Learn practical skills that not only fast-track your Dataproc expertise, but also help you succeed with a wide range of GCP technologies. Written by data experts Narasimha Sadineni and Anu Venkataraman, this cookbook tackles real-world use cases like serverless Spark jobs, Kubernetes-native deployments, and cost-optimized data lake workflows. You'll learn how to create ephemeral and persistent Dataproc clusters, run secure data science workloads, implement monitoring solutions, and plan effective migration and optimization strategies. Create Dataproc clusters on Compute Engine and Kubernetes Engine Run data science workloads on Dataproc Execute Spark jobs on Dataproc Serverless Optimize Dataproc clusters to be cost effective and performant Monitor Spark jobs in various ways Orchestrate various workloads and activities Use different methods for migrating data and workloads from existing Hadoop clusters to Dataproc
