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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with Data Exploration

Getting Started with Data Exploration

Installing Superset

Sharing Superset

Configuring Superset

Adding a database

Creating a visualization

Uploading a CSV

Configuring the table schema

Customizing the visualization

Making a dashboard

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

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

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

Visualizing Data in a Column

Visualizing Data in a Column

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

Comparing Feature Values

Comparing Feature Values

Comparing multiple time series

Comparing two time series

Identifying differences in trends for two feature values

Drawing Connections between Entity Columns

Drawing Connections between Entity Columns

Directed force networks

Sankey's diagram

Mapping Data That Has Location Information

Mapping Data That Has Location Information

Building Dashboards

Building Dashboards

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Caching queries

Superset uses Flask-Cache for cache management and Flask-Cache provides support for many backend implementations that fit different use cases.

Redis is the recommended cache backend for Superset. But if you do not expect many users to use your Superset installation, then FileSystemCache is a good alternative to a Redis server.

The following are some of the cache implementations that are available, with a description and their configuration variables:

CACHE_TYPE	Description and configuration
`simple`	Uses a local Python dictionary to store results. This is not really safe when using multiple workers on the web server.
`filesystem`	Uses the filesystem to store cached values. The `CACHE_DIR` variable is the directory path used by FileSystemCache.
`memcached`	Uses a memcached server to store values. Requires the `pylibmc` Python package installed in the...