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Simplify Big Data Analytics with Amazon EMR

You're reading from  Simplify Big Data Analytics with Amazon EMR

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781801071079
Pages 430 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sakti Mishra Sakti Mishra
Profile icon Sakti Mishra
LinkedIn
Sakti Mishra is an engineer, architect, author, and technology leader with over 16 years of experience in the IT industry. He is currently working as a senior data lab architect at Amazon Web Services (AWS). He is passionate about technologies and has expertise in big data, analytics, machine learning, artificial intelligence, graph networks, web/mobile applications, and cloud technologies such as AWS and Google Cloud Platform. Sakti has a bachelor’s degree in engineering and a master’s degree in business administration. He holds several certifications in Hadoop, Spark, AWS, and Google Cloud. He is also an author of multiple technology blogs, workshops, white papers and is a public speaker who represents AWS in various domains and events.
See other products by Sakti Mishra

Table of Contents (19) Chapters

Preface Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
Chapter 1: An Overview of Amazon EMR Chapter 2: Exploring the Architecture and Deployment Options Chapter 3: Common Use Cases and Architecture Patterns Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR Section 2: Configuration, Scaling, Data Security, and Governance
Chapter 5: Setting Up and Configuring EMR Clusters Chapter 6: Monitoring, Scaling, and High Availability Chapter 7: Understanding Security in Amazon EMR Chapter 8: Understanding Data Governance in Amazon EMR Section 3: Implementing Common Use Cases and Best Practices
Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR Chapter 14: Best Practices and Cost-Optimization Techniques Other Books You May Enjoy

Creating an EMR cluster and an EMR notebook

Before getting started with our use case, we need to create an EMR cluster and then create an EMR notebook that points to the EMR cluster we have created. Let's assume this EMR cluster is a long-running cluster that is active to support your development workloads as you plan to do interactive development with EMR notebooks.

Now let's learn how to create the EMR cluster and notebook.

Creating an EMR cluster

As explained in Chapter 5, Setting Up and Configuring EMR Clusters, to create an EMR cluster, follow these steps:

  1. Navigate to Amazon EMR's Create cluster screen at https://console.aws.amazon.com/elasticmapreduce/home?region=us-east-1#quick-create.
  2. Select Go to advanced options and, from the advanced options screen, select the latest stable release. We have selected the emr-6.4.0 release because that was the latest stable release while writing this chapter. From the Applications list, make sure you select...
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Authors (1)

Profile icon Sakti Mishra
LinkedIn
Sakti Mishra is an engineer, architect, author, and technology leader with over 16 years of experience in the IT industry. He is currently working as a senior data lab architect at Amazon Web Services (AWS). He is passionate about technologies and has expertise in big data, analytics, machine learning, artificial intelligence, graph networks, web/mobile applications, and cloud technologies such as AWS and Google Cloud Platform. Sakti has a bachelor’s degree in engineering and a master’s degree in business administration. He holds several certifications in Hadoop, Spark, AWS, and Google Cloud. He is also an author of multiple technology blogs, workshops, white papers and is a public speaker who represents AWS in various domains and events.
Read more
See other products by Sakti Mishra

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