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

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

Customizing the visualization

The Ethereum dataset has a Date (UTC) column, a UnixTimestamp column, and a value representing the total transaction volume in USD on that date. Let's chart the Value column in the latest Ethereum transaction history data:

Data form for the Ethereum transaction volume chart

The Data form calculates the sum of transactions grouped by dates. There is only one value over which the SUM(Value) aggregate function is computed:

Table chart with total value of Ethereum over the years

The sum of transaction values, grouped by the Date column value and sorted in descending order, shows that the busiest days in the Ethereum network are also the most recent.