Book description
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.
You'll learn how to:
- Design a modern and secure cloud native or hybrid data analytics and machine learning platform
- Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform
- Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities
- Enable your business to make decisions in real time using streaming pipelines
- Build an MLOps platform to move to a predictive and prescriptive analytics approach
Publisher resources
Table of contents
- Preface
- 1. Modernizing Your Data Platform: An Introductory Overview
-
2. Strategic Steps to Innovate with Data
- Step 1: Strategy and Planning
- Step 2: Reduce Total Cost of Ownership by Adopting a Cloud Approach
- Step 3: Break Down Silos
- Step 4: Make Decisions in Context Faster
- Step 5: Leapfrog with Packaged AI Solutions
- Step 6: Operationalize AI-Driven Workflows
-
Step 7: Product Management for Data
- Applying Product Management Principles to Data
- 1. Understand and Maintain a Map of Data Flows in the Enterprise
- 2. Identify Key Metrics
- 3. Agreed Criteria, Committed Roadmap, and Visionary Backlog
- 4. Build for the Customers You Have
- 5. Don’t Shift the Burden of Change Management
- 6. Interview Customers to Discover Their Data Needs
- 7. Whiteboard and Prototype Extensively
- 8. Build Only What Will Be Used Immediately
- 9. Standardize Common Entities and KPIs
- 10. Provide Self-Service Capabilities in Your Data Platform
- Summary
- 3. Designing for Your Data Team
- 4. A Migration Framework
- 5. Architecting a Data Lake
- 6. Innovating with an Enterprise Data Warehouse
- 7. Converging to a Lakehouse
- 8. Architectures for Streaming
- 9. Extending a Data Platform Using Hybrid and Edge
- 10. AI Application Architecture
- 11. Architecting an ML Platform
- 12. Data Platform Modernization: A Model Case
- Index
- About the Authors
Product information
- Title: Architecting Data and Machine Learning Platforms
- Author(s):
- Release date: October 2023
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098151614
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