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Modern Data Architecture on AWS

Modern Data Architecture on AWS

By : Behram Irani
4.9 (15)
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Modern Data Architecture on AWS

Modern Data Architecture on AWS

4.9 (15)
By: Behram Irani

Overview of this book

Many IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.
Table of Contents (24 chapters)
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1
Part 1: Foundational Data Lake
5
Part 2: Purpose-Built Services And Unified Data Access
17
Part 3: Govern, Scale, Optimize And Operationalize

Data processing using AWS Glue

If you recall our conversations from the last few chapters, we kept bringing up AWS Glue for multiple use cases, including for data catalogs, crawlers, classifiers, and batch ingestion using connectors. Now, we come to Glue ETL, which is the most distinct feature of Glue. Since Glue is a fully managed and serverless service, it excels in data transformation types of tasks, usually undertaken by data engineering personas in an organization. You can create Glue ETL jobs using Spark, Python, or Ray. Spark is a common platform for creating distributed computing-based ETL jobs. Since EMR also provides Spark and Glue also has Spark, in the following table, let’s try to simplify certain scenarios where you would prefer to use one over the other:

EMR typical usage

Glue ETL typical usage

Since EMR alleviates all the infrastructure and operational heavy...

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