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Serverless ETL and Analytics with AWS Glue

You're reading from Serverless ETL and Analytics with AWS Glue Your comprehensive reference guide to learning about AWS Glue and its features

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781800564985

Length 434 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Data Analysis

Authors (6):

Vishal Pathak

Ishan Gaur

Tomohiro Tanaka

Albert Quiroga

Subramanya Vajiraya

Noritaka Sekiyama

+2 more

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1 – Introduction, Concepts, and the Basics of AWS Glue

2. Chapter 1: Data Management – Introduction and Concepts FREE CHAPTER

3. Chapter 2: Introduction to Important AWS Glue Features

4. Chapter 3: Data Ingestion

5. Section 2 – Data Preparation, Management, and Security

6. Chapter 4: Data Preparation

7. Chapter 5: Data Layouts

8. Chapter 6: Data Management

9. Chapter 7: Metadata Management

10. Chapter 8: Data Security

11. Chapter 9: Data Sharing

12. Chapter 10: Data Pipeline Management

13. Section 3 – Tuning, Monitoring, Data Lake Common Scenarios, and Interesting Edge Cases

14. Chapter 11: Monitoring

15. Chapter 12: Tuning, Debugging, and Troubleshooting

16. Chapter 13: Data Analysis

17. Chapter 14: Machine Learning Integration

18. Chapter 15: Architecting Data Lakes for Real-World Scenarios and Edge Cases

19. Other Books You May Enjoy

Troubleshooting and debugging common issues in AWS Glue ETL

While AWS Glue makes it easy to implement data integration workloads using different components/microservices, depending on the user configuration and use case we may encounter a number of issues. In this section, we will discuss some common issues we may encounter while working with AWS Glue and different methods to solve those specific issues one by one.

ETL job failures

A Glue ETL job can fail for a number of reasons. Most job failures can be attributed to issues with configuration or resource provisioning, depending on the use case. Let’s explore some common issues we may come across while working with Glue ETL.

OOM errors

When working with a large volume of data, it is not uncommon for us to run into OOM errors. OOM errors can appear in both drivers and executors, depending on the use case. How we approach the issue largely depends on where exactly the issue is occurring, whether in the driver or the...

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Authors (6)

Vishal Pathak

Vishal Pathak

Vishal Pathak is a Data Lab Solutions Architect at AWS. Vishal works with customers on their use cases, architects solutions to solve their business problems, and helps them build scalable prototypes. Prior to his journey in AWS, Vishal helped customers implement business intelligence, data warehouse, and data lake projects in the US and Australia.

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Ishan Gaur

Ishan Gaur

Ishan Gaur has more than 13 years of IT experience in soft ware development and data engineering, building distributed systems and highly scalable ETL pipelines using Apache Spark, Scala, and various ETL tools such as Ab Initio and Datastage. He currently works at AWS as a senior big data cloud engineer and is an SME of AWS Glue. He is responsible for helping customers to build out large, scalable distributed systems and implement them in AWS cloud environments using various big data services, including EMR, Glue, and Athena, as well as other technologies, such as Apache Spark, Hadoop, and Hive.

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Albert Quiroga

Albert Quiroga

Albert Quiroga works as a senior solutions architect at Amazon, where he is helping to design and architect one of the largest data lakes in the world. Prior to that, he spent four years working at AWS, where he specialized in big data technologies such as EMR and Athena, and where he became an expert on AWS Glue. Albert has worked with several Fortune 500 companies on some of the largest data lakes in the world and has helped to launch and develop features for several AWS services.

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Tomohiro Tanaka

Tomohiro Tanaka

Tomohiro Tanaka is a senior cloud support engineer at AWS. He works to help customers solve their issues and build data lakes across AWS Glue, AWS IoT, and big data technologies such Apache Spark, Hadoop, and Iceberg.

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Noritaka Sekiyama

Noritaka Sekiyama

Noritaka Sekiyama is a Senior Big Data Architect on the AWS Glue and AWS Lake Formation team. He has 11 years of experience working in the software industry. Based in Tokyo, Japan, he is responsible for implementing software artifacts, building libraries, troubleshooting complex issues and helping guide customer architectures

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Subramanya Vajiraya

Subramanya Vajiraya

Subramanya Vajiraya is a Big data Cloud Engineer at AWS Sydney specializing in AWS Glue. He obtained his Bachelor of Engineering degree specializing in Information Science & Engineering from NMAM Institute of Technology, Nitte, KA, India (Visvesvaraya Technological University, Belgaum) in 2015 and obtained his Master of Information Technology degree specialized in Internetworking from the University of New South Wales, Sydney, Australia in 2017. He is passionate about helping customers solve challenging technical issues related to their ETL workload and implementing scalable data integration and analytics pipelines on AWS.

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