Vetr Delivers Financial Advice in Real-Time with Redis Enterprise Cloud
Company Size
11-200
Region
- America
Country
- United States
Product
- Redis Enterprise Cloud
- PostgreSQL
- Heroku
- CloudFlare
Tech Stack
- Java 8
- Node.js
- Angular JS
- Redis
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Infrastructure as a Service (IaaS) - Cloud Databases
- Infrastructure as a Service (IaaS) - Cloud Storage Services
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Finance & Insurance
- Software
Applicable Functions
- Business Operation
- Product Research & Development
Use Cases
- Real-Time Location System (RTLS)
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
Based out of New York City, Vetr is a modern research startup that has been taking the FinTech industry by storm since it was founded in 2013. Through simplified crowdsourced star-ratings for the stock market, Vetr delivers personalized insights and alerts about the people, stocks and funds its users follow. Vetr was born out of the frustration experienced by its founders when researching their own personal investment ventures. Message boards and online forums no longer provided quality information, putting them at a loss. Following the lead of popular online review platforms like Yelp, ZocDoc and Angie’s List, they decided to channel the new crowdsourcing frenzy into a new and valuable FinTech startup called Vetr.
The Challenge
Building the technology stack for this pioneering business was no small feat for Zoheb Sait, CTO of Vetr. Many years of software development and architecture experience helped him size up his architecture needs using a user and data centric approach. Sait designed a modern, flexible and scalable application that incorporated many types of data, all of which changed, but at different rates. Building a modern day Yelp for financial stocks required not just a great user experience, but extreme focus on accuracy. Delivering the most accurate and recent counts of users who voted, their ratings and rating summaries was critical because users were building investment strategies based on this information. Vetr also needed to retain data such as stock ticker symbols, user identities, user posts and followers. This translated into a set of needs around speed, scale and operational simplicity. Processing the rapidly changing data required rapid access and sub-millisecond speeds of updates. The business model was predicated on increasing numbers of users, followers, and constant updates reflecting real time activity. The technology platform underpinning this had to be easy to scale, easy to operate and have no downtime or data loss.
The Solution
Vetr implemented a hybrid database strategy, choosing Redis as its persistent, in-memory database for rapidly changing data and supplementing it with PostgreSQL to store static, structured data. Redis was chosen because of its blazing fast performance and in-built data structures. Vetr needed functionality that could process a variety of data, maintain atomic counters, sets of followers and followed, ratings aggregates and more, with extremely low latencies. They had some data they wanted cached, but other data that needed to be distributed and persisted. Redis, simple yet sophisticated was an obvious choice because of the data structures it offers. Today, Vetr uses almost every data structure offered by Redis. To save on operational costs, Zoheb chose going to the cloud. However, finding cloud services that could scale Redis infinitely and seamlessly as needed, without downtime was impossible – until he found Redis Lab’s Redis Enterprise Cloud. “Redis Enterprise Cloud offered something no one else did – zero downtime upgrades and downgrades” says Zoheb Sait, “Its ability to deliver high performance and scale seamlessly with no operational hassle was a huge selling point for us.” Redis Enterprise Cloud had the advantage that upgrades or downgrades required no downtime or dataset movement. It also offered two different plans: one for caching and the other for persistence, which fit Vetr’s data and pricing needs perfectly. Not only did the two plans offer competitively priced Redis, but also the additional advantages that Zoheb did not have to worry about managing and operating the infrastructure needed to run it. Redis Enterprise Cloud scaled seamlessly, shielding Vetr from any operational hassles related to managing this very important persistent data layer, which is crucial to their service.
Operational Impact
Quantitative Benefit
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