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You're reading from  Apache Superset Quick Start Guide

Product typeBook
Published inDec 2018
Reading LevelIntermediate
Publisher
ISBN-139781788992244
Edition1st Edition
Languages
Python
Tools
Apache Superset
Concepts
Business Intelligence
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Author (1)
Shashank Shekhar
Shashank Shekhar
author image
Shashank Shekhar

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.
Read more about Shashank Shekhar

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Summary

We understood that when the Superset web server is started we can configure it for our runtime environment needs using the superset_config.py file. We looked at the configuration parameters that can make Superset secure and scalable to match optimal trade-offs.

SQL Lab provides an opportunity to experiment with result sets before plotting. It can be used as an excellent tool for exploring datasets and developing charts.

In this chapter, we replaced SQLite metadata with a PostgreSQL database and configured a web app to use it as the database. So that the web app can handle many concurrent users, we deployed it on a Gunicorn server:

  • PostgreSQL metadata database
  • Gunicorn
  • NGINX
  • HTTPS authorization
  • Securing session data
  • Redis caching system
  • Celery for long-running queries
  • Mapbox access token

Nicely done! We have been able to make dashboards, use SQL Lab, and understand the...

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Apache Superset Quick Start Guide
Published in: Dec 2018Publisher: ISBN-13: 9781788992244
© 2018 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Shashank Shekhar

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.
Read more about Shashank Shekhar

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