A Step-by-Step Guide to Modern Cloud Analytics
With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Les mer
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With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Cloud vendors are offering modern data platforms for building cloud analytics solutions to collect data and consolidate into single storage solutions that provide insights for business users. The core of any analytics framework is the data warehouse, and previously customers did not have many choices of platform to use.
Snowflake was built specifically for the cloud and it is a true game changer for the analytics market. This book will help onboard you to Snowflake, present best practices to deploy, and use the Snowflake data warehouse. In addition, it covers modern analytics architecture and use cases. It provides use cases of integration with leading analytics software such as Matillion ETL, Tableau, and Databricks. Finally, it covers migration scenarios for on-premise legacy data warehouses.
What You Will Learn
Know the key functionalities of Snowflake
Set up security and access with cluster
Bulk load data into Snowflake using the COPY command
Migrate from a legacy data warehouse to Snowflake
integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools
Who This Book Is For
Those working with data warehouse and business intelligence (BI) technologies, and existing and potential Snowflake users
This chapter will help reader learn about the cloud analytics market and learn about key players as well as see the advantages of Snowflake.
Chapter 2: Getting Started with Snowflake
This will help to start with Snowflake. It will guide you to launch Snowflake and explain the web interface and functionality. In addition, it will provide guidance for the snowflake pricing model and available options for Virtual Warehouse.
Chapter 3: Working with Virtual Data Warehouse
This chapter will introduce the key component on snowflake - the Virtual Warehouse. The reader will learn how to work and monitor Virtual Warehouse.
Chapter 4: Loading Data into Snowflake
This chapter will show how you can bulk load data into Snowflake using COPY command.
Chapter 5: Getting started with SnowSQL
Chapter Goal: This chapter will introduce snowflake CLI tool. The reader will learn how to work with CLI tool and what he can accomplish with it.
Chapter 6: Loading Continuously Using Snowpipe
This chapter will introduce another Snowflake utility - Snowpipe. The reader will learn how to use Snowpipe and will create datapipeline in order to loading data into Snowflake.
Chapter 7: Snowflake Administration
This will guide reader who to manage Snowflake Account.
Chapter 8: Snowflake Security
This chapter will cover fundamental principles of the Snowflake security model and will guide reader in how to setup security and access with cluster.
Chapter 9: Working with Semi Structure and Binary Data
Snowflake allows you to work with semi-structure data. This chapter will demonstrate you how to work with popular semi-structure formats like JSON, XML and AVRO. In addition, the user will learn about Binary data and to work with it at Snowflake.
Chapter 10: Working Snowflake Time Travel and Data Sharing
This chapter will introduce unique functionality of Snowflake - Time Travel. The reader will learn how and when they may need to use this functionality. In addition, the reader will learn about another unique functionality - Data Sharing.
Chapter 11: Building Modern Analytics Solution with Matillion ETL, Tableau, and Snowflake
This chapter will help users to learn about integration of Snowflake data platform with modern Business Intelligence and Data Integration tools (they are both key partners of Snowflake and leaders at the market).
Chapter 12: Snowflake for Big Data Use Cases
This chapter will show you common bigdata use cases and demonstrate how Snowflake can handle Big Data.
Chapter 13: Legacy DW migration to the Snowflake
This chapter will demonstrate migration process of legacy DW into Snowflake
Dmitry Shirokov is a Data Architect & Cloud Analytics Consultant at Rock Your Data, focused on digital transformation, design analytics solutions, data integration and migration, data governance, and cloud/in-house infrastructure. With over 10 years of experience in data analytics and big data, he has the breadth and depth of experience needed to build mature analytical solutions. Before joining Rock Your Data in early 2018, he worked in different companies in tech consulting and banking sectors. Previously, he held various data-engineering positions focusing on data-driven business transformation.
Donna Strok loves all things data and for over 10 years has worked in the field with companies such as Expedia Group, JPMorgan Chase and most recently Amazon. She earned her Bachelors degree in Computer Science and her Masters in Computer Information Systems.
She resides in beautiful Seattle, WA with her cat Dwayne Johnson and in her free time enjoys the wanderlust of world travel. She's always on the on the hunt for exploring unique grocery stores and amazing hole-in the-wall restaurants, where some of her most memorable meals have been had.