Architecting data and machine learning platforms : enable analytics and AI-driven innovation in the cloud /
Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner.
- First edition.
- xviii, 338 pages : illustrations ; 24 cm
Includes index.
Modernizing your data platform : an introductory overview -- Strategic steps to innovate with data -- Designing your data team -- A migration framework -- Architecting a data lake -- Innovating with an enterprise data warehouse -- Converging to a lakehouse -- Architectures for streaming -- Extending a data platform using hybrid and edge -- AI application architecture -- Architecting an ML platform -- Data platform modernization : a model case.
All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage.