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Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards Shashank Shekhar - okladka książki

Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards Shashank Shekhar - okladka książki

Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards Shashank Shekhar - audiobook MP3

Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards Shashank Shekhar - audiobook CD

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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.

First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe.

You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data.

Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers.

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O autorze książki

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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