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Data Engineering with AWS Cookbook

You're reading from Data Engineering with AWS Cookbook A recipe-based approach to help you tackle data engineering problems with AWS services

Product type Paperback

Published in Nov 2024

Publisher Packt

ISBN-13 9781805127284

Length 528 pages

Edition 1st Edition

Languages

Python

Tools

AWS Glue

Concepts

Data Engineering

Authors (4):

Viquar Khan

Gonzalo Herreros González

Huda Nofal

Trâm Ngọc Phạm

View More author details

Table of Contents (16) Chapters

Preface

1. Chapter 1: Managing Data Lake Storage

2. Chapter 2: Sharing Your Data Across Environments and Accounts FREE CHAPTER

3. Chapter 3: Ingesting and Transforming Your Data with AWS Glue

4. Chapter 4: A Deep Dive into AWS Orchestration Frameworks

5. Chapter 5: Running Big Data Workloads with Amazon EMR

6. Chapter 6: Governing Your Platform

7. Chapter 7: Data Quality Management

8. Chapter 8: DevOps – Defining IaC and Building CI/CD Pipelines

9. Chapter 9: Monitoring Data Lake Cloud Infrastructure

10. Chapter 10: Building a Serving Layer with AWS Analytics Services

11. Chapter 11: Migrating to AWS – Steps, Strategies, and Best Practices for Modernizing Your Analytics and Big Data Workloads

12. Chapter 12: Harnessing the Power of AWS for Seamless Data Warehouse Migration

13. Chapter 13: Strategizing Hadoop Migrations – Cost, Data, and Workflow Modernization with AWS

14. Index

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15. Other Books You May Enjoy

Protecting your cluster from security vulnerabilities

Historically, each version of EMR was provided with an AMI, on which it was tested. In addition, on provisioning the image, it automatically checks the repository for security updates. You can only disable that behavior using repo-upgrade-on-boot=NONE. Remember that when using a custom AMI, you must take the responsibility of keeping the image patched. However, patches that affect the kernel require a restart to be installed. Thus, in the past, you had two options:

Upgrade to a newer version of EMR, which means taking upgrades in all components and services. This could cause your application to need to be retested and would potentially require a migration.
Indicate EMR to use a newer AMI and risk running the services on an image on which it has never been tested.

That has changed since EMR 5.36 and 6.6, if using EMR 5 or 6, respectively. From those versions onwards, you can let EMR upgrade the AMI automatically...

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Authors (4)

Trâm Ngọc Phạm

Trâm Ngọc Phạm

Trâm Ngọc Phạm is a senior data architect with over a decade of hands-on experience working in the big data and AI field, from playing a lead role in tailoring cloud data platforms to BI and analytics use cases for enterprises in Vietnam. While working as a Senior Data and Analytics consultant for the AWS Professional Services team, she specialized in guiding finance and telco companies across Southeast Asian countries to build enterprise-scale data platforms and drive analytics use cases that utilized AWS services and big data tools.

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Viquar Khan

Viquar Khan

Viquar Khan is a senior data architect at AWS Professional Services and brings over 20 years of expertise in finance and data analytics, empowering global financial institutions to harness the full potential of AWS technologies. He designs cutting-edge, customized data solutions tailored to complex industry needs. A polyglot developer skilled in Java, Scala, Python, and other languages, Viquar has excelled in various technical roles. As an expert group member of JSR368 (JavaTM Message Service 2.1), he has shaped industry standards and actively contributes to open source projects such as Apache Spark and Terraform. His technical insights have reached and benefited over 6.7 million users on Stack Overflow.

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Huda Nofal

Huda Nofal

Huda Nofal is a seasoned data engineer with over 7 years of experience at Amazon, where she has played a key role in helping internal business teams achieve their data goals. With deep expertise in AWS services, she has successfully designed and implemented data pipelines that power critical decision-making processes across various organizations. Huda's work primarily focuses on leveraging Redshift, Glue, data lakes, and Lambda to create scalable, efficient data solutions.

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Gonzalo Herreros González

Gonzalo Herreros González

Gonzalo Herreros González is a principal data architect. He holds a bachelor's degree in computer science and a master's degree in data analytics. He has experience of over a decade in big data and two decades of software development, both in AWS and on-premises. Previously, he worked at MasterCard where he achieved the first PCI-DSS Hadoop cluster in the world. More recently, he worked at AWS for over 6 years, building data pipelines for the internal network data, and later, as an architect in the AWS Glue service team, building transforms for AWS Glue Studio and helping large customers succeed with AWS data services.

See other products by Gonzalo Herreros González

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