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Snowflake Cookbook: Techniques for building modern cloud data warehousing solutions
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About Snowflake Cookbook: Techniques For Building Modern
Product Description 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 operationsKey FeaturesBuild and scale modern data solutions using the all-in-one Snowflake platformPerform advanced cloud analytics for implementing big data and data science solutionsMake quicker and better-informed business decisions by uncovering key insights from your dataBook DescriptionSnowflake 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 learnGet to grips with data warehousing techniques aligned with Snowflake's cloud architectureBroaden your skills as a data warehouse designer to cover the Snowflake ecosystemTransfer skills from on-premise data warehousing to the Snowflake cloud analytics platformOptimize performance and costs associated with a Snowflake solutionStage data on object stores and load it into SnowflakeSecure data and share it efficiently for accessManage transactions and extend Snowflake using stored proceduresExtend cloud data applications using Spark ConnectorWho this book is forThis 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 ContentsGetting Started with SnowflakeManaging the Data Life CycleLoading and Extracting Data into and out of SnowflakeBuilding Data Pipelines in SnowflakeData Protection and Security in SnowflakePerformance and Cost OptimizationSecure Data SharingBack to the Future with Time TravelAdvanced SQL TechniquesExtending Snowflake's Capabilities About the Author 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 lear
