Skip to content
Superset Quick Start Guide, published by Packt
Jupyter Notebook Python Shell
Branch: master
Clone or download
suwarnarajput Update
Latest commit f8c3739 Jan 2, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
Chapter01 Code files added Dec 14, 2018
Chapter02 Code files added Dec 14, 2018
Chapter03 Code files added Dec 14, 2018
Chapter04 Code files added Dec 14, 2018
Chapter05 Code files added Dec 14, 2018
Chapter06 Code files added Dec 14, 2018
Chapter07 Code files added Dec 14, 2018
Graphics Revision Dec 13, 2018
LICENSE Initial commit May 4, 2018 Update Jan 2, 2019

Apache Superset Quick Start Guide

Apache Superset Quick Start Guide

This is the code repository for Apache Superset Quick Start Guide, published by Packt.

Develop interactive visualizations by creating user-friendly dashboards

What is this book about?

Apache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset.

This book covers the following exciting features:

  • Get to grips with the fundamentals of data exploration using Superset
  • Set up a working instance of Superset on cloud services like Google Compute Engine
  • Integrate Superset with SQL databases
  • Build dashboards with Superset
  • Calculate statistics in Superset for numerical, categorical, or text data

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

gunicorn -w 3 
 -k gevent 
 --timeout 120 

Following is what you need for this book: This book is for data analysts, BI professionals, and developers who want to learn Apache Superset. If you want to create interactive dashboards from SQL databases, this book is what you need. Working knowledge of Python will be an advantage but not necessary to understand this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

Software and Hardware List

Chapter Software required OS required
1-8 Superset Ubuntu
2 Google Compute Engine Ubuntu
2 NGINX Ubuntu
2 Postgres Ubuntu
2 Redis Ubuntu
2 Celery Ubuntu

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Related products

Get to Know the Author

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.

Suggestions and Feedback

Click here if you have any feedback or suggestions.

You can’t perform that action at this time.