0

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

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

Chapter 3: Data Ingestion

In the previous chapter, we discussed the fundamental concepts and inner workings of the various features/microservices that are available in AWS Glue, such as Glue Data Catalog, connections, crawlers, and classifiers, the schema registry, Glue ETL jobs, development endpoints, interactive sessions, and triggers. We also explored how AWS Glue crawlers aid in data discovery by crawling different types of data stores – Amazon S3, JDBC (Amazon RDS or on-premises databases), and DynamoDB/MongoDB/DocumentDB infer the schema and populate AWS Glue Data Catalog. While discussing Glue ETL in the previous chapter, we introduced a few of the important extensions/features of Spark ETL, including GlueContext, DynamicFrame, JobBookmark, and GlueParquet. In this chapter, we will see them in action by looking at some examples.

In this chapter, we will be discussing some of the components of AWS Glue mentioned in the previous paragraph – specifically Glue...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at £16.99/month. Cancel anytime

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.

See other products by Vishal Pathak

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.

See other products by Ishan Gaur

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.

See other products by Albert Quiroga

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.

See other products by Tomohiro Tanaka

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

See other products by Noritaka Sekiyama

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.

See other products by Subramanya Vajiraya

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Data Governance Handbook

Data Governance Handbook

This book provides a highly focused view of real business outcomes powered by data governance, that resonate with non-data executives such as CFOs and CEOs. You'll also find useful insights into how to implement data governance initiatives.

May 2024 13h 8m

Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

This book shows you how to use Apache Spark, Delta Lake, and Databricks to build data pipelines, manage and transform data, optimize performance, and more. Additionally, you'll implement DataOps and DevOps practices, and orchestrate data workflows.

May 2024 14h 36m

Azure Data Engineer Associate Certification Guide

Azure Data Engineer Associate Certification Guide

Unlock the power of Azure data engineering with this certification guide, elevating your skills in data processing, storage, and security with the help of practical insights, hands-on exercises, and the latest advancements.

May 2024 18h 16m

Microsoft Power BI Cookbook

Microsoft Power BI Cookbook

Microsoft Power BI is the most sought-after platform for BI professionals' visualization needs. Explore the latest Power BI features, future AI enhancements, and integration with other Power Platform tools via new recipes in this updated edition.

Jul 2024 19h 56m

Python Data Cleaning Cookbook

Python Data Cleaning Cookbook

The book shows you how to clean, wrangle, and view data from multiple perspectives, including dataset and column attributes. You will cover common and not-so-common challenges that are faced while cleaning messy data for complex situations and learn to manipulate data to get it down to a form that can be useful for making the right decisions.

May 2024 16h 12m

Microsoft Azure AI Fundamentals AI-900 Exam Guide

Microsoft Azure AI Fundamentals AI-900 Exam Guide

This AI-900 study guide will help you prepare and practice for the certification exam. You'll delve into AI workloads, ML principles, computer vision, NLP, knowledge mining, and generative AI using Azure cloud services.

May 2024 9h 36m

Using Stable Diffusion with Python

Using Stable Diffusion with Python

This book shows you how to use Python to control Stable Diffusion and generate high-quality images. In addition to covering the basic usage of the diffusers package, the book provides solutions for extending the package for more advanced purposes.

Jun 2024 11h 44m

Getting Started with DuckDB

Getting Started with DuckDB

This hands-on book teaches you to analyze large datasets with blazing speed and ease. You will learn how to use DuckDB to quickly load, query, transform, analyze, and visualize data effectively through a series of practical examples.

Jun 2024 12h 44m

Databricks Certified Associate Developer for Apache Spark Using Python

Databricks Certified Associate Developer for Apache Spark Using Python

This guide gets you ready for certification with expert-backed content, key exam concepts, and topic reviews. Additionally, you'll be able to make the most of Apache Spark 3.0 to modernize workloads and more using specific tools and techniques.