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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

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

Limitations of Amazon EMR and possible workarounds

Understanding best practices is very important as that helps to optimize your usage in AWS and will give you the best performance and cost optimization. Apart from best practices, it is also important to understand different limitations the service has so that you can plan for alternate workarounds.

The following are some of the limitations that you should consider while implementing big data workloads in Amazon EMR:

  • S3 throughput: When you are writing to or reading from S3, there are a few API limits that you should be aware of. S3 has a limit of 3,500 PUT/POST/DELETE requests per second per prefix in a bucket and 5,500 GET requests per second per prefix in a bucket. These limits are per S3 prefix but there is no limit on how many prefixes you might have. So, as a workaround, you should think of having more prefixes and leverage a partition or sub-partition structure while storing data in S3. As an example, if you have...
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