Book Image

Apache Superset Quick Start Guide

By : Shashank Shekhar
Book Image

Apache Superset Quick Start Guide

By: Shashank Shekhar

Overview of this book

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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Table of Contents (10 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Data Exploration
Getting Started with Data Exploration
Datasets
Installing Superset
Sharing Superset
Configuring Superset
Adding a database
Adding a table
Creating a visualization
Uploading a CSV
Configuring the table schema
Customizing the visualization
Making a dashboard
Summary
2
Configuring Superset and Using SQL Lab
Configuring Superset and Using SQL Lab
Setting the web server
Creating the metadata database
Migrating data from SQLite to PostgreSQL
Web server
Setting up an NGINX reverse proxy
Setting up HTTPS or SSL certification
Flask-AppBuilder permissions
Securing session data
Caching queries
Mapbox access token
Long-running queries
Main configuration file
SQL Lab
Summary
3
User Authentication and Permissions
User Authentication and Permissions
Security features
Setting up OAuth Google sign-in
List Users page
List Base Permissions page
Views/Menus page
List Permissions on Views/Menus pages
Alpha and gamma – building blocks for custom roles
User Statistics page
Action log
Summary
4
Visualizing Data in a Column
Visualizing Data in a Column
Dataset
Distribution – histogram
Comparison – relationship between feature values
Comparison – box plots for groups of feature values
Comparison – side-by-side visualization of two feature values
Summary statistics – headline
Summary
5
Comparing Feature Values
Comparing Feature Values
Dataset
Comparing multiple time series
Comparing two time series
Identifying differences in trends for two feature values
Summary
6
Drawing Connections between Entity Columns
Drawing Connections between Entity Columns
Datasets
Directed force networks
Chord diagrams
Sunburst chart
Sankey's diagram
Partitioning
Summary
7
Mapping Data That Has Location Information
Mapping Data That Has Location Information
Data
Scatter point
Scatter grid
Arcs
Path
Summary
8
Building Dashboards
Building Dashboards
Charts
Dashboards
Summary
9
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Dataset

A favorite on my blogroll at https://austinrochford.com/ is a good place to discover intuitive explanations of Bayesian machine learning methods. In the December 29, 2017 blog post Quantifying Three Years of Reading (https://austinrochford.com/posts/2017-12-29-quantifying-reading.html), the blogger analyzes changes in their own reading log dataset. The reading log is available as a Google sheet at this link: https://docs.google.com/spreadsheets/d/1wNbJv1Zf4Oichj3-dEQXE_lXVCwuYQjaoyU1gGQQqk4/.

The reading log is a time series dataset, which the blogger frequently updates. I have taken a snapshot of the reading log and saved it in the chapter's GitHub directory, https://github.com/PacktPublishing/Superset-Quick-Start-Guide/Chapter04/. The dataset has been modified by the addition of a new column. You can run the Jupyter Notebook generate_dataset.ipynb to generate the...

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