Brian Dreyer

We wanted a scalable analytics and business intelligence data store for our product built natively on AWS. We also wanted a service that could ingest data from any data source (SQL, NoSQL, AI outputs, etc), not only traditional relational database. We have a native transformation service built into our product, but #AWSElasticsearch was a natural fit for the analytics storage service to use for our analysis and visualization features. It also gave us flexibility in implementation to make sure it worked for our needs, it scales and cost is easily manageable.

We had a goal of taking advantage of AWS services to create a unique offering and #AWSLambda gave us a chance to create an embedded analytics and business intelligence solution that was completely different from any other on the market. The big problem with server-based analytics products is that those servers are always on, and consequently you're paying for that server to sit there the majority of each day doing nothing. With serverless technology, we created an analytics solutions that works just like any other SaaS analytics product, but you pay for what you use. Using #AWSLambda and #AWSElasticContainerService we created an analytics product for companies of any size with a cost of hosting that is typically about half of a traditional server-based deployed product.