Snowflake Cookbook: Strategic and practical recipes for building governed, intelligent, AI-ready data platforms

Build modern data and AI platforms in the Snowflake AI Data Cloud with practical recipes for data governance, automation, Cortex AI, observability, and intelligent application development.

Key Features

• Apply 100+ practical recipes for data engineering, data governance, and AI development
• Build secure, scalable data solutions using Snowflake platform capabilities
• Learn when and why to apply each pattern for real-world success
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Snowflake Cookbook, Second Edition is your practical guide to building modern data and AI platforms on the Snowflake AI Data Cloud. Through more than 100 hands-on recipes, you’ll learn how to implement Snowflake capabilities and when to apply them to solve real-world challenges.

This edition reflects Snowflake’s evolution into a unified platform for data, AI, and application development. You’ll build secure, governed data platforms using roles, policies, tagging, classification, and data sharing. From data ingestion and transformation to automation with stages, tasks, streams, and Openflow, you’ll learn proven patterns for building scalable data and AI solutions.

You’ll work with structured, semi-structured, and unstructured data while exploring capabilities such as cloning, Time Travel, governance, and operational best practices for reliability and resilience. The book also introduces Snowflake Cortex and AI-powered capabilities, showing you how to build intelligent apps, AI-ready architectures, and enterprise AI solutions.

Written by a 30-year data industry veteran and multi-year Snowflake Data Superhero, this edition combines practical implementation guidance with architectural insight. By the end of the book, you’ll be equipped to design, build, govern, and operate trusted data and AI platforms that deliver business value.

What you will learn

• Build secure Snowflake environments using roles and policies
• Create automated data pipelines with stages, tasks, and streams
• Ingest and transform data using Openflow and Snowflake
• Implement data governance with tags, masking policies, and classification
• Work with structured, semi-structured, and unstructured data
• Build AI-ready solutions using Snowflake Cortex
• Improve resilience with cloning and Time Travel
• Learn when and why to apply each pattern effectively

Who this book is for

This book is for data professionals designing, building, and operating modern data and AI platforms on the Snowflake AI Data Cloud. Data engineers, data architects, analytics engineers, AI engineers, developers, and technical leaders will gain practical implementation skills and architectural insight. Basic SQL and data warehousing knowledge is recommended. Whether you’re building governed data platforms, AI-ready architectures, intelligent applications, or enterprise analytics solutions, this book provides proven patterns and practical, real-world guidance.

Table of Contents

1. Getting Started with Snowflake
2. Managing the Data Life Cycle
3. Loading and Extracting Data Into and Out of Snowflake
4. Building Data Pipelines in Snowflake
5. Data Protection and Security in Snowflake
6. Performance and Cost Optimization
7. Secure Data Sharing
8. Back to the Future with Time Travel
9. Advanced SQL Techniques
10. Extending Snowflake Capabilities
11. Introduction to Snowflake Cortex

Keith Belanger is Field CTO at DataOps live and a multi-year Snowflake Data Superhero. With nearly 30 years in data architecture and engineering, he helps global enterprises modernize through DataOps, governance, and AI-ready design. Keith is an expert in Data Modeling, trained in Kimball methodology, and Data Vault 2.0 Certified. A frequent speaker and trusted advisor, he bridges hands-on execution with executive-level strategy to help organizations turn the Snowflake AI Data Cloud into a foundation for innovation and intelligent decision-making.

Hamid Qureshi is a senior cloud and data warehouse professional with almost two decades of total experience, having architected, designed, and led the implementation of several data warehouse and business intelligence solutions. He has extensive experience and certifications across various data analytics platforms, ranging from Teradata, Oracle, and Hadoop to modern, cloud-based tools such as Snowflake. Having worked extensively with traditional technologies, combined with his knowledge of modern platforms, he has accumulated substantial practical expertise in data warehousing and analytics in Snowflake, which he has subsequently captured in his publications.

Hammad Sharif is an experienced data architect with more than a decade of experience in the information domain, covering governance, warehousing, data lakes, streaming data, and machine learning. He has worked with a leading data warehouse vendor for a decade as part of a professional services organization, advising customers in telco, retail, life sciences, and financial industries located in Asia, Europe, and Australia during presales and post-sales implementation cycles. Hammad holds an MSc. in computer science and has published conference papers in the domains of machine learning, sensor networks, software engineering, and remote sensing.