How CogniFit uses AWS.

CogniFit Incorporates AWS Technology Simplify, Streamline, & Supercharge Our Data

From the very beginning, CogniFit has been keenly aware of the importance of using good data. Our tools for cognitive assessment and stimulation are conceptualized, planned, and developed with accurate, scientific data in mind, which has allowed our platform to grow into a tool used not only by individuals around the world who want to give themselves or their loved ones a brain boost, but also education and healthcare professionals who need a powerful platform for assessing, tracking, and training the cognitive abilities of their students and clients.

But that isn’t the only way CogniFit creates value through data. The massive amount of data generated by our platform can be used by scientific researchers as well, providing a unique source of information on cognitive abilities and how these abilities play an integral role in everything we do. CogniFit has chosen to use Amazon Web Service platform to create a valuable data ecosystem, allowing us to simplify our data storage processes, streamline the way we use and interact with our data, and supercharge our ability to create value from data for our customers and our research partners.

How CogniFit uses Amazon Web Services To Bring Even More Value to Our Cognitive Data

We chose to use Amazon’s AWS platform as a key part of our data infrastructure because of the power and relative simplicity of their platform. Here are a few examples of how we use AWS:

Simplifying Data Storage

CogniFit’s physical data is split between two main databases which keep separate personal information used for applications such as registering on our website and making payments from the data created by our applications which include information which might indicate, for example, a user’s physical or mental health status.

While there are multiple reasons for the way our physical data storage is organized, the personal privacy of our users is paramount among these. However, there may be times when we need to compare data which might reside on one database, such as a user’s ability to visually focus on a single stimulus, with data from another, such as age.

By using the AWS Database Migration Service to create data buckets in the Simple Storage Service, we are able to create a simple and safe data ‘sandbox’ where we can manipulate our data without putting our user’s information at risk.

In addition, we use Amazon’s RDS (Relational Database Service) to help us simplify the way we manage our databases. Using the AWS EC2 (Elastic Compute Cloud) to host our Front-End servers, we are able to take advantage of powerful load-balancing and autoscaling features to adapt our system to the variable traffic demands throughout the day, meaning we can seamlessly provide our users with peak performance during the high traffic periods without wasting server resources during low traffic periods.

Not only does AWS provide us with fantastic tools to create a powerful, efficient, and flexible data storage plan, through Amazon’s WAF (Web Application Firewall), we are able to ensure our web apps are safe and secure from online threats.

Streamlining Data Processing

With the data tools such as AWS Glue, we are able to refine, filter, and process data in new and powerful ways, allowing us to turn raw data into organized, valuable information.

The creation of virtual databases using tools such as the AWS Glue Crawler and the AWS Glue ETL Jobs allows us to build simple yet powerful sources of data for a variety of internal and external applications.

In this way, we can build individualized databases, specifically designed to meet the requirements of each data application.  

Supercharging Data Analysis

Of course, data—even perfectly organized data—isn’t worth anything if we aren’t able to understand it and see the stories it is trying to tell. That’s where AWS tools such as SageMaker, Athena, and QuickSight come in.

If the tools in the toolchest of AWS Glue helped us turn data into information, these tools help us turn information into insights.

SageMaker is giving our data science department and software development teams the ability to create hyper-personalized recommendations and adjust complexity and difficulty of cognitive tasks on the fly to give our users the best possible experience and outcomes.

In addition, the business insights coming from QuickSight help us to understand our business like never before, shining new light on our user’s behaviors and needs.

But gathering and processing data is only one part of how we create incredible value for our partners and customers. Our priority is to deliver powerful solutions based on our unique cognitive data.

Amazon’s Cloudfront CDN allows us to deliver data, applications, and APIs to our researcher and developer partners globally with low latency and high transfer speeds, as well as to deliver engaging and challenging cognitive training and assessment tools to our customers safely, effectively, and quickly.

Conclusion

Amazon Web Services has allowed us to push our data even further than before. The integration of tools such as Simple Storage Service, AWS Glue Crawler, and SageMaker into our data infrastructure has unlocked new potential for our data.