Book Image

Cloud Scale Analytics with Azure Data Services

By : Patrik Borosch
Book Image

Cloud Scale Analytics with Azure Data Services

By: Patrik Borosch

Overview of this book

Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs.
Table of Contents (20 chapters)
1
Section 1: Data Warehousing and Considerations Regarding Cloud Computing
4
Section 2: The Storage Layer
7
Section 3: Cloud-Scale Data Integration and Data Transformation
14
Section 4: Data Presentation, Dashboarding, and Distribution

Understanding the loading strategy with Synapse-dedicated SQL pools

The different options that you have available for the table design of a dedicated SQL pool, distributed or replicated tables, and the decision regarding the use of column stores or heaps and partitioning on top will influence the way in which you load data into it.

Certainly, loading into a hash-distributed table can be quite a quick process. But when you consider the additional compute step to calculate the hash keys to distribute the incoming rows to their target distribution and compare it to a round-robin-distributed table, where this step is not required, you can imagine that loading data into the latter will be faster.

Another consideration for a staging table in a dedicated SQL pool would be to use heap tables instead of column store ones. Again, you can avoid additional compute overhead for the column store and load data quickly.

In the end, it all comes down to performance. Therefore, following...