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Flask JSONDash
Flask JSONDash

6
23
+ 1
2
Superset
Superset

72
119
+ 1
8
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Flask JSONDash vs Superset: What are the differences?

Flask JSONDash: A flask app to make dashboards, easily. Easily configurable, chart dashboards from any arbitrary API endpoint. JSON config only. Ready to go; Superset: Data exploration and visualization platform, by Airbnb. 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.

Flask JSONDash and Superset can be primarily classified as "Business Intelligence" tools.

Flask JSONDash and Superset are both open source tools. It seems that Superset with 25.1K GitHub stars and 4.83K forks on GitHub has more adoption than Flask JSONDash with 2.89K GitHub stars and 250 GitHub forks.

What is Flask JSONDash?

Easily configurable, chart dashboards from any arbitrary API endpoint. JSON config only. Ready to go.

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.
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Why do developers choose Flask JSONDash?
Why do developers choose Superset?

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        What tools integrate with Flask JSONDash?
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          What are some alternatives to Flask JSONDash and Superset?
          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.
          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.
          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.
          Data Studio
          Unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions. It’s easy and free.
          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.
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          Decisions about Flask JSONDash and Superset
          Julien DeFrance
          Julien DeFrance
          Principal Software Engineer at Tophatter · | 16 upvotes · 488.2K views
          atSmartZipSmartZip
          Rails
          Rails
          Rails API
          Rails API
          AWS Elastic Beanstalk
          AWS Elastic Beanstalk
          Capistrano
          Capistrano
          Docker
          Docker
          Amazon S3
          Amazon S3
          Amazon RDS
          Amazon RDS
          MySQL
          MySQL
          Amazon RDS for Aurora
          Amazon RDS for Aurora
          Amazon ElastiCache
          Amazon ElastiCache
          Memcached
          Memcached
          Amazon CloudFront
          Amazon CloudFront
          Segment
          Segment
          Zapier
          Zapier
          Amazon Redshift
          Amazon Redshift
          Amazon Quicksight
          Amazon Quicksight
          Superset
          Superset
          Elasticsearch
          Elasticsearch
          Amazon Elasticsearch Service
          Amazon Elasticsearch Service
          New Relic
          New Relic
          AWS Lambda
          AWS Lambda
          Node.js
          Node.js
          Ruby
          Ruby
          Amazon DynamoDB
          Amazon DynamoDB
          Algolia
          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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