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Simplify Big Data Analytics with Amazon EMR

You're reading from   Simplify Big Data Analytics with Amazon EMR A beginner's guide to learning and implementing Amazon EMR for building data analytics solutions

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801071079
Length 430 pages
Edition 1st Edition
Concepts
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Author (1):
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Sakti Mishra Sakti Mishra
Author Profile Icon Sakti Mishra
Sakti Mishra
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
2. Chapter 1: An Overview of Amazon EMR FREE CHAPTER 3. Chapter 2: Exploring the Architecture and Deployment Options 4. Chapter 3: Common Use Cases and Architecture Patterns 5. Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR 6. Section 2: Configuration, Scaling, Data Security, and Governance
7. Chapter 5: Setting Up and Configuring EMR Clusters 8. Chapter 6: Monitoring, Scaling, and High Availability 9. Chapter 7: Understanding Security in Amazon EMR 10. Chapter 8: Understanding Data Governance in Amazon EMR 11. Section 3: Implementing Common Use Cases and Best Practices
12. Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark 13. Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming 14. Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi 15. Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA 16. Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR 17. Chapter 14: Best Practices and Cost-Optimization Techniques 18. Other Books You May Enjoy

Using S3 versus HDFS for cluster storage

As you may have understood by now, EMR has the flexibility to choose HDFS or EMRFS + S3 as the cluster's persistent storage. As explained previously, EMR has different types of nodes: the master node, core nodes, and task nodes.

Now, let's understand how both of these storage layers are different and which problem statements they solve.

HDFS as cluster-persistent storage

As you can see from the following diagram, there are multiple core nodes pointing to the master node, and each core node has its own CPU, memory, and HDFS storage:

Figure 2.4 – EMR node structure with HDFS as persistent storage

These are some properties to be aware of when your cluster uses HDFS as persistent storage:

  • You need to maintain by default three copies of data across the core nodes to be fault-tolerant.
  • An EMR cluster is deployed in a single Availability Zone (AZ) of a Region, so a complete AZ failure...
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