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 lakes

Simply put, a data lake is a centralized repository to store all kinds of data. Data can be structured (such as relational database data in tabular format), semi-structured (such as JSON), or unstructured (such as images, PDFs, and so on). Data from all the heterogenous source systems is collected and processed in this single repository and consumed from it. In its early days, Apache Hadoop became the go-to place for setting up data lakes. The Hadoop framework provided a storage layer called Hadoop Distributed File System (HDFS) and a data processing layer called MapReduce. Organizations started using this data lake as a central place for storing and processing all kinds of data. The data lake provided a great alternative to storing and processing data outside relational databases and data warehouses. But soon, the data lake setup on-premises infrastructure became a nightmare. We will look at those challenges as we build upon this chapter.

The following diagram shows...