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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

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

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Visualizing data

We can use popular Python libraries such as Bokeh, Matplotlib, and Plotly to make visualizations in Azure Databricks. In this section, we will learn how we can use these libraries in Azure Databricks and how we can make use of notebook features to work with these visualizations.

Bokeh

Bokeh is a Python interactive data visualization library used to create beautiful and versatile graphics, dashboards, and plots.

To use Bokeh, you can install the Bokeh PyPI package either by installing it at the cluster level through the libraries UI and attaching it to your cluster or by installing it at the notebook level using pip commands.

Once we have installed the library and we can import it into our notebook, to display a Bokeh plot in Databricks, we must first create the plot and generate an HTML file embedded with the data for the plot, created, for example, by using Bokeh's file_html or output_file functions, and then pass this HTML to the Databricks displayHTML...

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Authors (1)

Alan Bernardo 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.

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