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

Author (1)

Shashank Shekhar

Making a dashboard

Making a dashboard in Superset is quick and easy. Just go to Dashboards and create a new dashboard. In the form, fill in the Title and a string value in the Slug field, which will be used to create the dashboard's URL, and hit Save:

Edit Dashboard form

Open the dashboard and select the Edit Dashboard option. Because we have two seemingly unrelated datasets, we can use the Tabs dashboard component to see them one at a time:

Insert components list when editing dashboard

Once you have added a Tabs component, insert the two charts you just made using the Your charts & filters option:

Dashboard for Chapter 1: Getting Started

The dashboard URL syntax is http://{address}:{port number}/superset/dashboard/getting-started. Replace the address and port number variables with the appropriate values, and you can use this link to open or share your dashboard.

Our dashboard is ready and live for users with accounts on the web server. In the next chapter, we will learn about user roles. After that, you will able to get your favorite collaborators to register. With them on board, you can start collaborating on charts and dashboards for your data analysis projects.

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Apache Superset Quick Start Guide

Published in: Dec 2018Publisher: ISBN-13: 9781788992244

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Author (1)

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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