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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
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Author (1)
Shashank Shekhar
Shashank Shekhar
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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

That must have felt productive, since we were able to create our dashboard from nothing in Superset.

Before we summarize what we have just finished in this chapter, it is important that we discuss when Superset might not be the right visualization tool for a data analysis project.

Visualization of data requires data aggregation. Data aggregation is a function of one or more column values in tables. A group by operation is applied on a particular column to create groups of observations, which are then replaced with the summary statistics defined by the data aggregation function. Superset provides many data aggregation functions; however, it has limited usability when hierarchical data aggregation is required for visualizations.

Hierarchical data aggregation is the process of taking a large amount of rows in a table and displaying summaries of partitions and their sub-partitions. This is not an option in Superset for most of the visualizations.

Also, Superset has limited customization options on the design and formatting of visualizations. It supports changes in color schemes and axis label formatting. Individuals or teams who want to tinker and optimize the visual representation of their data will find Superset very limited for their needs.

Finally, it's time to summarize our achievements. We have been able to install Superset, add a database, create a dashboard, and share it with users. We are now ready to add additional databases and tables, and create new visualizations and dashboards. Exploring data and telling data stories with Superset dashboards is one of your skill sets now!

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Apache Superset Quick Start Guide
Published in: Dec 2018Publisher: ISBN-13: 9781788992244
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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