Deciphering Data Architectures
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Deciphering Data Architectures
Author: James Serra
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2024-02-06
Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll: Gain a working understanding of several data architectures Learn the strengths and weaknesses of each approach Distinguish data architecture theory from reality Pick the best architecture for your use case Understand the differences between data warehouses and data lakes Learn common data architecture concepts to help you build better solutions Explore the historical evolution and characteristics of data architectures Learn essentials of running an architecture design session, team organization, and project success factors Free from product discussions, this book will serve as a timeless resource for years to come.
Data Architecture
Data is an expensive and expansive asset. Information hunger has forced retention capacity from megabytes to terabytes of data. Millions of dollars are spent accumulating data, and millions more are paid to the professional staff that nurture, secure, and extract information out of these billions of bytes of data. To ensure that it is usable, data must be structured in a flexible manner that is responsive to change, and is readily available for access. This book explains the principles underlying data architecture, how data evolves with organizations, the challenges organizations face in structuring and managing data, and the proven methods and technologies to solve these complex issues. The author takes a holistic approach to the field of data architecture from various applied perspectives, including data modeling, data quality, enterprise information management, database design, data warehousing, and data governance. Key Features Explains the fundamental concepts of enterprise architecture through definitions and real-world scenarios Teaches data managers and planners how to build a data architecture roadmap, structure the right team, and build a set of solutions for the various challenges they face Offers concise case studies that highlight how fundamental principles are put into practice.
Building Medallion Architectures
Author: Piethein Strengholt
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2025-03-28
In today's data-driven world, organizations must manage and analyze vast amounts of information to deliver the insights that give them a competitive advantage. Many turn to the medallion architecture because it's a proven and well-known design. Yet implementing a robust data pipeline can be difficult, particularly when it comes to using the medallion architecture's bronze, silver, and gold layers—done wrong, it can hamper your ability to make data-driven decisions. This practical guide helps you build a medallion architecture the right way with Azure Databricks and Microsoft Fabric. Drawing on hands-on experience from the field, Piethein Strengholt demystifies common assumptions and complex problems you'll face when embarking on a new data architecture. Architects and engineers of all stripes will find answers to the most typical questions along with insights from real organizations about what's worked, what hasn't, and why. You'll learn: Lakehouse and medallion architecture fundamentals and key concepts Design considerations for Azure Databricks and Microsoft Fabric Scaling considerations, including governance, security, automation, and more How to make informed decisions when designing or implementing new data architectures Proven patterns for success that align with broader organizational objectives