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Data exploration and visualization platform, by Airbnb

What is Superset?

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.
Superset is a tool in the Business Intelligence category of a tech stack.
Superset is an open source tool with 24.8K GitHub stars and 4.8K GitHub forks. Here’s a link to Superset's open source repository on GitHub

Who uses Superset?

Companies
18 companies use Superset in their tech stacks, including Airbnb, Dial Once, and Streamroot.

Developers
5 developers use Superset.

Superset Integrations

Why developers like Superset?

Here’s a list of reasons why companies and developers use Superset
Superset Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Superset in their tech stack.

Julien DeFrance
Julien DeFrance
Full Stack Engineering Manager at ValiMail · | 16 upvotes · 66.6K views
atSmartZip
Amazon DynamoDB
Ruby
Node.js
AWS Lambda
New Relic
Amazon Elasticsearch Service
Elasticsearch
Superset
Amazon Quicksight
Amazon Redshift
Zapier
Segment
Amazon CloudFront
Memcached
Amazon ElastiCache
Amazon RDS for Aurora
MySQL
Amazon RDS
Amazon S3
Docker
Capistrano
AWS Elastic Beanstalk
Rails API
Rails
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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Superset's features

  • A rich set of visualizations to analyze your data, as well as a flexible way to extend the capabilities
  • An extensible, high granularity security model allowing intricate rules on who can access which features, and integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuiler)
  • A simple semantic layer, allowing to control how data sources are displayed in the UI, by defining which fields should show up in which dropdown and which aggregation and function (metrics) are made available to the user
  • Deep integration with Druid allows for Caravel to stay blazing fast while slicing and dicing large, realtime datasets

Superset Alternatives & Comparisons

What are some alternatives to Superset?
Looker
We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.
Metabase
Metabase is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.
Grafana
Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
Tableau
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Redash
Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.
See all alternatives

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