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

Chapter 10: Loading the Presentation Layer

Now that you have seen the many modules that you have available on Azure to build your modern data warehouse architecture, let's dive into the Presentation layer in this chapter and the one following, Chapter 11, Developing and Maintaining the Presentation Layer.

In this chapter, you are going to learn how to load data into your Presentation Layer using either PolyBase, the COPY command, or, alternatively, Synapse pipelines.

You will see how you can include Synapse serverless SQL pools and examine the benefits of using this tool, implementing SQL directly in your data lake.

Additionally, we will investigate how you can leverage Spark to extend the functionality of your data loads beyond the SQL language.

Finally, we will examine the options in terms of Synapse regarding metadata exchange between the different compute components and how this will increase developer efficiency.

The topics covered in this chapter are as...