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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

Understanding Amazon EMR integration with Apache Ranger

Apache Ranger is an open source framework that provides comprehensive security across the Hadoop ecosystem, using which you can define and manage security policies to control access on Hadoop components.

Starting from the EMR 5.32.0 release, your EMR cluster has default native integration with Apache Ranger. That means EMR installs and manages the Ranger plugin on your behalf.

Similar to AWS Lake Formation, Apache Ranger also provides fine-grained access control on top of Hive Metastore or Amazon S3 prefixes. Using Ranger, you can define access permissions on top of Hive databases, tables, or columns while using Hive queries or Spark jobs. Data masking and row-level filtering are only supported with Hive.

Ranger has the following two primary components:

  • Apache Ranger policy admin server: With this server, you can define authorization policies for Hive Metastore, Apache Spark, and EMRFS with S3. To integrate...
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