Book Image

Modern Data Architecture on AWS

By : Behram Irani
5 (1)
Book Image

Modern Data Architecture on AWS

5 (1)
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)
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 ingestion using AWS Glue

In our data lake in Chapter 2, we introduced Glue Data Catalog, which is one of the key components of data lake design. Glue is also a popular ETL tool for data engineers, who want to ingest data from the source systems and transform the data as it flows between the different layers of the data lake. Glue provides complete flexibility to deal with any kind of data engineering complexity. In essence, Glue ETL can help extract data from any source system, transform it, and load it into any target system.

Since this chapter is all about batch data ingestion and we want to keep most of our focus on ingesting data into the data lake in S3, we will focus on those use cases. We have a dedicated chapter for data processing later, where we will revisit Glue ETL.

Use case for data ingestion using modern ETL techniques

The business at GreatFin wants to derive value from all the data available in its existing data stores; some are stored in older-generation...