Architecting Data Solutions with Snowflake

Modern businesses need scalable, cost-efficient data platforms; however, traditional, fragmented systems are complex and expensive. Snowflake provides a modern, cloud-native, fully managed solution, simplifying data architecture while delivering performance and flexibility. This book helps readers leverage Snowflake's full potential for advanced, scalable data solutions.

This book begins by walking you through Snowflake account configuration and its unique three-layered architecture, introducing key architectural pillars. The book covers building data engineering pipelines using various methods like Snowpipe and Snowpark, differentiating between ETL and ELT patterns. Finally, you will learn to architect diverse data patterns, such as data warehouses, data lakes, data mesh, and lakehouses, and explore Snowpark for machine learning, Snowflake Horizon, Cortex, and building generative AI and LLM solutions.

This book equips you with the knowledge to design and implement modern data solutions, including data warehouses, lakes, and mesh patterns, using Snowflake. You will be well-equipped to tackle complex data architecture challenges and drive innovation in any data-driven environment.

WHAT YOU WILL LEARN
● Learn to design data platform solutions with Snowflake.
● Design scalable and cost-effective data architectures.
● Implement efficient data ingestion and extraction pipelines.
● Implement modern data patterns, including data warehouse, data lake, and data mesh, using Snowflake’s flexible architecture.
● Apply modern data governance and security practices.
● Design AI/ML workloads using Snowflake Cortex.

WHO THIS BOOK IS FOR
This book is designed for data enthusiasts, architects, and engineers who are looking to build modern, cloud-native data platforms using Snowflake. Whether you are designing your first data solution or optimizing complex enterprise architectures, this book offers practical insights, patterns, and real-world examples to elevate your data skills.

1. Navigating Snowflake Account Setup and Configuration
2. Unraveling the Three-Tier Architecture
3. The Pillars of Architectural Excellence
4. Understanding Snowflake's Security Features
5. Implementing Data Governance
6. Evaluating and Optimizing Snowflake’s Performance
7. Unlocking Snowflake’s Cost and Performance
8. Implementing Data Integrations
9. Designing Data Solutions
10. Designing Data Engineering Pipelines
11. Designing ETL and ELT With Snowflake
12. Architecting Data Warehouse
13. Implementing Data Lake Solutions
14. Exploring Data Mesh Design Options
15. Building Data Lakehouses
16. Embracing Snowpark and Snowpark ML
17. Architecting LLM Solutions with Snowflake
18. Unleashing Snowflake’s Advanced Capabilities

Pooja Kelgaonkar is a recognized leader in the data architecture and engineering space, and one of the esteemed Snowflake Data Superheroes. With nearly two decades of industry experience, she has built a reputation as a trusted expert in data modernization, distributed systems, and cloud-native data platforms. Over the course of her career, she has held key roles across diverse technology stacks, designing and implementing scalable data solutions that drive business transformation.

Currently serving as a senior data architect at Rackspace Technology, Pooja leads enterprise-grade data implementations with a focus on performance, scalability, and seamless integration. Her strategic guidance plays a pivotal role in enabling clients to achieve their data-driven goals through optimized architecture and modern data platform design. Beyond her professional achievements, Pooja is a passionate advocate for continuous learning and community engagement. Her thought leadership is reflected in her technical blogs on Snowflake and Google Cloud Platform (GCP), many of which are regularly featured by Snowflake and Google in their community channels.

A frequent public speaker, Pooja actively participates in conferences, tech webinars, and community events, where she shares practical insights and architectural best practices. Her deep commitment to education, mentorship, and community growth underscores her mission to empower the next generation of data professionals and contribute to the ongoing evolution of the data ecosystem.