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 federation using Amazon Athena

Amazon Athena is primarily used to query data from S3 data lakes. However, to query data across heterogeneous sources, Athena provides a feature called Federated Query. This feature enables different personas, such as data analysts, data engineers, and data scientists, to execute queries across disparate data sources from Athena itself. The single biggest differentiator for Federated Query is that the execution of such queries happens inside the systems that store the data.

Athena executes these federated queries using connectors. Athena provides many connectors to a variety of source systems. Using these connectors, Athena can pass portions of the query that need to be executed in the source system. This execution is assisted by AWS Lambda functions, which optimize the query’s execution and gather the data received from the underlying systems. Since Lambda functions are serverless and scalable, this allows Athena to query larger datasets...