Architecting Data and Machine Learning Platforms (Third Early Release)

All cloud architects need to know how to build data platforms—the key to enabling businesses with data and delivering enterprise-wide intelligence in a fast and efficient way. This handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks.

Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle in a cloud environment, from ingestion to activation, using real-world enterprise architectures. You'll learn how to transform 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.

This book shows you how to

Design a modern cloud native or hybrid data analytics and machine learning platform
Accelerate data-led innovation by consolidating enterprise data in a data platform
Democratize access to enterprise data and allow business teams to extract insights and build AI/ML capabilities
Enable your business to make decisions in real time using streaming pipelines
Move from a descriptive analytics approach to a more predictive and prescriptive one by building an MLOps platform
Make your organization more effective in working with data analytics and machine learning in a cloud environment