Architecting Data and Machine Learning Platforms (Second 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. What is a data platform? Why do you need it? What does building a data and ML platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that we recommend you take to build data and ML capabilities in your business, and wrap up all the concepts in a model data modernization case. 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 Who is this book for? This book is for architects who wish to support data-driven decision making in their business by creating a data and ML platform using public cloud technologies. It is also relevant for a data engineer, data analyst, data scientist, or ML engineer, who will find several useful concepts to gain a high-level design view of the systems that they might be implementing on top of.