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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

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781838647216
Length 414 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

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

Working with VNets in Azure Databricks

Azure Databricks can be deployed within a custom virtual network. This is called VNet injection and is very important from a security perspective. When we deploy with default settings, inbound traffic is closed, but outbound traffic is open without restrictions. When we use VNet injection and we deploy directly to a custom virtual network, we can apply the same security policies around all our Azure Services, to meet compliance and security requirements.  

In case you are working in data science or exploratory environments, it's good to leave the outbound traffic open to be able to download packages and libraries for Python, R, and Maven, and Ubuntu packages also.

As we have mentioned before, Azure Databricks works on two planes of service. The first is the control page, which we use through the Databricks API to work with workspace assets. The second is the data plane where the clusters are deployed. It is this second plane...

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