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

Technical requirements

There are not too many technical requirements! All that you will require in order to follow this chapter is the following:

An Azure subscription, where you have at least contributor rights or you are the owner.
Your Synapse workspace or the right to provision an Azure Data Factory service.
An Azure DevOps Git or GitHub account. This is optional, required only if you want to integrate your data factory with a DevOps repository.
Azure Storage Explorer: You will need this tool to interact with your data lake, for example. Please download it from here: https://azure.microsoft.com/en-us/features/storage-explorer/.
Azure Data Lake Storage and two folders, one for the source and one for a target. Refer to Chapter 3, Understanding the Data Lake Storage Layer, for a refresher.
An arbitrary file that you will upload to your source folder and use in your first copy job.
Access to an Azure DevOps or GitHub repository.

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

Table of Contents (20) Chapters

Technical requirements

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Table of Contents (20) Chapters

Technical requirements

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