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

Big Data

Author (1):

Sakti Mishra

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

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

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Reference architecture for genomics data analytics

Before going into the technical implementation details of genomics data analytics, let's understand what genomics means. It is a field of study of biology that focuses on the evolution, mapping, structure, and functions of genomes. A genome is a complete set of DNA of a living being, which includes all of its genes.

In recent times, there have been significant investments in genomics and clinical data to explore more about living beings' genes and their characteristics, which can help diagnose any disease beforehand or predict new features. Technology continues to play a vital role in genomics studies: as the data volume grows, you can use big data technologies for distributed processing.

Genomics datasets are available in complex data formats, such as VCF and gVCF, and to parse them, there are several popular frameworks available, such as Glow and Hail.