This means that you could store data in your own storage layer (e.g. In 2022, Snowflake announced support for Iceberg (an ACID file format similar to Delta Lake and Hudi). Only a Snowflake compute could access the Snowflake storage layer. In addition to the deep examples on Databricks in the book, there is coverage of alternative platforms such as Synapse Analytics and Snowflake so that you can make the right platform choice for your needs. The only difference is that Snowflake used to maintain a tight grip on the data access to the storage layer. Extensive coverage of Delta Lake ensures that you are aware of and can benefit from all that this new, open source storage layer can offer.
The patterns of success that you acquire from reading this book will help you hone your skills to build high-performing and scalable ACID-compliant lakehouses using flexible and cost-efficient decoupled storage and compute capabilities.
#DATA LAKEHOUSE SNOWFLAKE HOW TO#
And you will follow along with practical, scenario-based examples showing how to apply the capabilities of Delta Lake and Apache Spark to optimize performance, and secure, share, and manage a high volume, high velocity, and high variety of data in your lakehouse with ease. You will learn to write efficient PySpark code for batch and streaming ELT jobs on Azure. This book teaches you the intricate details of the Data Lakehouse Paradigm and how to efficiently design a cloud-based data lakehouse using highly performant and cutting-edge Apache Spark capabilities using Azure Databricks, Azure Synapse Analytics, and Snowflake.
Design and implement a modern data lakehouse on the Azure Data Platform using Delta Lake, Apache Spark, Azure Databricks, Azure Synapse Analytics, and Snowflake.
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