Amazon Quicksight聽vs聽Chartio聽vs聽Geckoboard

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Amazon Quicksight
Amazon Quicksight

43
59
+ 1
1
Chartio
Chartio

45
35
+ 1
6
Geckoboard
Geckoboard

81
45
+ 1
6
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- No public GitHub repository available -
- No public GitHub repository available -

What is Amazon Quicksight?

Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data.

What is Chartio?

Chartio is a cloud-based business analytics solution on a mission to enable everyone within an organization to access, explore, transform and visualize their data.

What is Geckoboard?

From uptime and analytics to check-ins, Geckoboard is a hosted dashboard that鈥檚 available on any screen with a browser. Geckoboard monitors your businesses vital signs.
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Why do developers choose Amazon Quicksight?
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        What are some alternatives to Amazon Quicksight, Chartio, and Geckoboard?
        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.
        DOMO
        Domo: business intelligence, data visualization, dashboards and reporting all together. Simplify your big data and improve your business with Domo's agile and mobile-ready platform.
        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.
        Power BI
        It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
        Amazon Athena
        Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
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        Decisions about Amazon Quicksight, Chartio, and Geckoboard
        Julien DeFrance
        Julien DeFrance
        Principal Software Engineer at Tophatter | 16 upvotes 909.1K 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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