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Distributed Data Systems with Azure Databricks

You're reading from Distributed Data Systems with Azure Databricks Create, deploy, and manage enterprise data pipelines

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781838647216

Length 414 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Data Science

Author (1):

Alan Bernardo Palacio

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Introducing Databricks

2. Chapter 1: Introduction to Azure Databricks FREE CHAPTER

3. Chapter 2: Creating an Azure Databricks Workspace

4. Section 2: Data Pipelines with Databricks

5. Chapter 3: Creating ETL Operations with Azure Databricks

6. Chapter 4: Delta Lake with Azure Databricks

7. Chapter 5: Introducing Delta Engine

8. Chapter 6: Introducing Structured Streaming

9. Section 3: Machine and Deep Learning with Databricks

10. Chapter 7: Using Python Libraries in Azure Databricks

11. Chapter 8: Databricks Runtime for Machine Learning

12. Chapter 9: Databricks Runtime for Deep Learning

13. Chapter 10: Model Tracking and Tuning in Azure Databricks

14. Chapter 11: Managing and Serving Models with MLflow and MLeap

15. Chapter 12: Distributed Deep Learning in Azure Databricks

16. Other Books You May Enjoy

Chapter 2: Creating an Azure Databricks Workspace

In this chapter, we will apply all the concepts we explored in Chapter 1, Introduction to Azure Databricks. We will create our first Azure Databricks workspace using the UI, and then explore the different possibilities of resource management through the Azure CLI, how to deploy these resources using the ARM template, and how we can integrate Azure Databricks within our virtual network using VNet injection.

In this chapter, we will discuss the following topics:

Using the Azure portal UI
Examining Azure Databricks authentication
Working with VNets in Azure Databricks
Azure Resource Manager templates
Setting up the Azure Databricks CLI

We will first begin by creating our workspace from the Azure portal UI.

The rest of the chapter is locked

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Authors (1)

Palacio

Palacio

Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.

See other products by Palacio

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