Snowflake Cookbook: Techniques for building modern cloud data warehousing solutions

Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operations

Key Features

• Build and scale modern data solutions using the all-in-one Snowflake platform
• Perform advanced cloud analytics for implementing big data and data science solutions
• Make quicker and better-informed business decisions by uncovering key insights from your data

Book Description

Snowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud. This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses.

You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses. You will then learn how to configure a virtual warehouse for optimizing cost and performance. Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data.

As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability. You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization.

By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes.

What you will learn

• Get to grips with data warehousing techniques aligned with Snowflake's cloud architecture
• Broaden your skills as a data warehouse designer to cover the Snowflake ecosystem
• Transfer skills from on-premise data warehousing to the Snowflake cloud analytics platform
• Optimize performance and costs associated with a Snowflake solution
• Stage data on object stores and load it into Snowflake
• Secure data and share it efficiently for access
• Manage transactions and extend Snowflake using stored procedures
• Extend cloud data applications using Spark Connector

Who this book is for

This book is for data warehouse developers, data analysts, database administrators, and anyone involved in designing, implementing, and optimizing a Snowflake data warehouse. Knowledge of data warehousing and database and cloud concepts will be useful. Basic familiarity with Snowflake is beneficial, but not necessary.

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's Capabilities

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.