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 lake patterns

There are two types of data lake patterns, as follows:

  • Centralized pattern
  • Distributed pattern

Let’s discuss each of them. Note that you can use a hybrid pattern too, depending on your use case.

Centralized pattern

In a centralized pattern, the business data is stored and accessed from a central location, to be used throughout the enterprise. For example, it may be easy to manage entity information in a centralized location; entity information such as name, address, gender, age, and profession of a person. It’s easier to manage such datasets in a centralized way, from a governance point of view as well as to avoid data duplication.

Certain LOBs may have additional properties of the data that are relevant only to their use cases. For example, the marketing department may also want to see customer lifetime value (CLV), net promoter score (NPS), marketing preferences, and so on for a person. These additional attributes can then...