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

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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...