This is the code repository for Apache Superset Quick Start Guide, published by Packt.
Develop interactive visualizations by creating user-friendly dashboards
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!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
export SUPERSET_UPDATE_PERMS=0
gunicorn -w 3
-k gevent
--timeout 120
-b 0.0.0.0:8088
superset:app
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).
| 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.
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.
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