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Distributed Data Systems with Azure Databricks

You're reading from  Distributed Data Systems with Azure Databricks

Product type Book
Published in May 2021
Publisher Packt
ISBN-13 9781838647216
Pages 414 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Alan Bernardo Palacio Alan Bernardo Palacio
Profile icon Alan Bernardo Palacio
LinkedIn
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 Alan Bernardo Palacio

Table of Contents (17) Chapters

Preface Section 1: Introducing Databricks
Chapter 1: Introduction to Azure Databricks Chapter 2: Creating an Azure Databricks Workspace Section 2: Data Pipelines with Databricks
Chapter 3: Creating ETL Operations with Azure Databricks Chapter 4: Delta Lake with Azure Databricks Chapter 5: Introducing Delta Engine Chapter 6: Introducing Structured Streaming Section 3: Machine and Deep Learning with Databricks
Chapter 7: Using Python Libraries in Azure Databricks Chapter 8: Databricks Runtime for Machine Learning Chapter 9: Databricks Runtime for Deep Learning Chapter 10: Model Tracking and Tuning in Azure Databricks Chapter 11: Managing and Serving Models with MLflow and MLeap Chapter 12: Distributed Deep Learning in Azure Databricks 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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Authors (1)

Profile icon Alan Bernardo Palacio
LinkedIn
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.
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See other products by Alan Bernardo Palacio

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