You're reading from Cloud Scale Analytics with Azure Data Services Build modern data warehouses on Microsoft Azure

Product type Paperback

Published in Jul 2021

Publisher Packt

ISBN-13 9781800562936

Length 520 pages

Edition 1st Edition

Tools

Azure

Concepts

Data Streaming

Author (1):

Patrik Borosch

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Table of Contents (20) Chapters

Preface

1. Section 1: Data Warehousing and Considerations Regarding Cloud Computing

2. Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses FREE CHAPTER

3. Chapter 2: Connecting Requirements and Technology

4. Section 2: The Storage Layer

5. Chapter 3: Understanding the Data Lake Storage Layer

6. Chapter 4: Understanding Synapse SQL Pools and SQL Options

7. Section 3: Cloud-Scale Data Integration and Data Transformation

8. Chapter 5: Integrating Data into Your Modern Data Warehouse

9. Chapter 6: Using Synapse Spark Pools

10. Chapter 7: Using Databricks Spark Clusters

11. Chapter 8: Streaming Data into Your MDWH

12. Chapter 9: Integrating Azure Cognitive Services and Machine Learning

13. Chapter 10: Loading the Presentation Layer

14. Section 4: Data Presentation, Dashboarding, and Distribution

15. Chapter 11: Developing and Maintaining the Presentation Layer

16. Chapter 12: Distributing Data

17. Chapter 13: Introducing Industry Data Models

18. Chapter 14: Establishing Data Governance

19. Other Books You May Enjoy

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...