Transition from Snowflake user to enterprise platform engineer by mastering real-world patterns for ingestion, transformation, orchestration, AI integration, and multi-tenant design.
Key Features
- Design enterprise architectures by following the Native Snowflake First decision hierarchy
- Develop production pipelines using dbt, SQLMesh, Snowpark, Tasks, and external orchestrators
- Utilize Cortex AI for analytics and Snowflake Cortex Code to accelerate Snowflake adoption
Book Description
Enterprise data teams face a crucial challenge: bridging the gap between basic Snowflake use and managing production-scale platforms. This book serves as a vital operations manual for enterprise Snowflake, authored by a Snowflake Data Expert with over 20 years of experience. It offers a practitioner-focused overview of the entire data engineering lifecycle—from open lakehouse models with Apache Iceberg, to real-time streaming with Snowpipe and Kafka, and AI-driven transformations with Snowflake Cortex. The focus is on integration, illustrating how native features and ecosystem tools work together effortlessly: dbt runs on Snowflake's compute layer, orchestration is flexible, and Terraform manages resources alongside cloud infrastructure. You'll learn to combine these tools effectively for the best results. The book also encourages AI-accelerated development, highlighting how agentic coding tools like Cortex Code, Cursor, and GitHub Copilot can greatly increase workflow speed—from creating dbt models and testing data with Snowflake MCP to automating infrastructure. By the end, you'll be capable of designing multi-tenant platforms, automating infrastructure with CI/CD, and enabling self-service analytics with Cortex Analyst and data sharing.
What you will learn
- Build lakehouse architectures using Apache Iceberg and native tables
- Develop real-time pipelines with Snowpipe Streaming and OpenFlow
- Apply data transformations through dbt, SQLMesh, and Snowpark Python
- Manage workflows with Tasks, Airflow, Prefect, and Dagster
- Utilize Cortex AI for text analysis and natural language processing
- Automate infrastructure setup with Terraform and CI/CD pipelines
- Design multi-tenant platforms with governance and cost management
- Facilitate self-service analytics through data sharing and BI tools
Who this book is for
This book targets senior data engineers with over three years of experience who are modernizing or consolidating data infrastructure onto Snowflake. It also benefits platform engineers automating Snowflake environments with CI/CD, technical leads assessing migration strategies from Spark, Oracle, or Redshift, and MLOps engineers building data pipelines. A solid understanding of SQL, basic Python skills, and familiarity with Snowflake concepts (such as warehouses, schemas, and stages) are expected. Experience with Spark, Terraform, or CI/CD pipelines is advantageous but not essential.
