Qrvey

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.

At Qrvey we moved from a SaaS application running in AWS to a deployed model where we would deploy the complete infrastructure and code to a customer's AWS account. This created a unique challenge as we were Cloud Native and hence were using a lot of AWS Services like Amazon DynamoDB, AWS Fargate , Amazon Elasticsearch Service, etc. We decided to first build AWS CloudFormation templates to convert all our infrastructure into code. Then created a AWS CloudFormation template that would first generate a AWS CodePipeline into a customer's AWS account. This pipeline would then deploy our Infrastructure AWS CloudFormation template and the code on that Infrastructure. This simplified and completely automated our upgrade process as well.