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

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801071079

Length 430 pages

Edition 1st Edition

Tools

Amazon EMR

Concepts

Big Data

Author (1):

Sakti Mishra

View More author details

Table of Contents (19) Chapters

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

Machine learning frameworks available in EMR

There are several machine learning libraries or frameworks that you can configure in your EMR cluster. TensorFlow and MXNet are a couple of popular ones, which are available as applications that you can choose while creating the cluster.

Even though TensorFlow and MXNet are available as pre-configured machine learning frameworks in EMR, you do have the option to configure other alternatives such as PyTorch and Keras as custom libraries.

Now let's get an overview of the TensorFlow and MXNet applications in EMR.

TensorFlow

TensorFlow is an open source platform using which you can develop machine learning models. It provides tools, libraries, and a community of resources that will help researchers and data scientists to easily develop and deploy machine learning models.

TensorFlow has been available in EMR since the 5.17.0 release and the recent 6.3.0 release includes TensorFlow v2.4.1.

If you plan to configure TensorFlow...