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

Summary

In this chapter, you examined the Synapse Analytics database relational storage option. You learned about the MPP architecture and the control and compute nodes, as well as how tables can be distributed or replicated in a database and how partitioning influences the data in the database.

You read about the CCI and how you can benefit from its performance.

Furthermore, you learned about resource allocation, concurrency, and the limits of the DWUc configurations. We touched on workload management and how you can optimize your database workload before you need to scale the database and pay more money for it. But when you need to scale for more concurrency and memory, and therefore more performance, you now know where to search for this functionality.

At the end, you covered the basics of loading data and why it is a good idea to use the COPY statement. You then learned how to maintain your statistics and how to rebuild your CCI to optimize it.

Finally, we compared...