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Indigo Dataproc Cookbook: Running Spark and Hadoop Workloads Google Cloud | Erin Mills Town Centre
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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
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
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