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Amazon Quicksight

Fast, easy to use business analytics at 1/10th the cost of traditional BI solutions
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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.
Amazon Quicksight is a tool in the Business Intelligence category of a tech stack.

Who uses Amazon Quicksight?

Companies
15 companies reportedly use Amazon Quicksight in their tech stacks, including Haymarket Media Asia, MPOWER Financing, and KTech.

Developers
24 developers on StackShare have stated that they use Amazon Quicksight.

Why developers like Amazon Quicksight?

Here鈥檚 a list of reasons why companies and developers use Amazon Quicksight
Top Reasons
Amazon Quicksight Reviews

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

Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 16 upvotes 505.5K 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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Amazon Quicksight Alternatives & Comparisons

What are some alternatives to Amazon Quicksight?
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.
See all alternatives

Amazon Quicksight's Followers
45 developers follow Amazon Quicksight to keep up with related blogs and decisions.
praveen kumar
lukeon kim
dongzhenye
Matt Sears
Himansu Sekhar
Anubhav Tyagi
John Alton
imlazypanda007
Mohamma76685757
Gopi K