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 patterns

One of the most complex and time-consuming parts of data warehouse modernization is data onboarding. Data can be onboarded in many different ways, using many different services. It all boils down to the requirement and the need for onboarding data in a particular manner. Let’s explore some typical data onboarding patterns for Amazon Redshift.

Data ingestion using AWS DMS

Let’s start with a use case first, so that the importance of DMS can be better understood when it comes to loading data into Redshift.

Use case for batch loading data into Amazon Redshift

GreatFin uses multiple databases and traditional data warehouses for their enterprise analytics reporting needs. They want to modernize their data warehouse using Amazon Redshift and would like to bulk load all the historic data from these existing systems into Redshift. They are looking for a fast, easy, and cost-effective way to do this in Redshift.

As you may recall from our...