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

The need for streaming architectures and its challenges

Many times, as time passes by, the value of insights from data diminishes. Figure 4.1 represents the value of data to the decision-making process where, as time passes by, its value decreases:

Figure 4.1 – Time value of data toward decision making

Figure 4.1 – Time value of data toward decision making

For organizations to do real-time analytics, data needs to be ingested from the source, processed immediately, and stored in the destination as soon as the event occurs. This allows organizations to derive insights from the data in real time. The need to get data in real time has many advantages:

  • Getting data in real time for analytics helps businesses make faster decisions and stay ahead of the competition
  • Analyzing real-time data allows early detection of security threats and anomalies in the data
  • IoT systems continuously send data in the form of events, and all this data needs to be captured and stored for analytics
  • Log data...