Capital One: Customer Success Story
Time series data at Capital One consists of infrastructure, application and business process metrics. The combination of these metrics are what the internal stakeholders rely on for observability which allows them to deliver better service and uptime for their customers, so protecting this critical data with a proven and tested recovery plan is not a “nice to have” but a “must have.”
The members of IT staff, Saravanan Krisharaju, Rajeev, and Karl, built a fault-tolerant solution based on InfluxDB Enterprise and AWS that collects and stores metrics and events, and added to this, machine learning, which uses the collected time series to model predictions which are then brought back into InfluxDB for real-time access. In addition to architecting and building this solution, they planned and executed on their disaster recovery plan.
Capital One uses InfluxDB to store and visualize all their business transaction, infrastructure health, and application performance, and service adoption metrics that are visualized in Grafana.
The Capital One IT team set out to build an architecture that would meet their business objectives. Designing and building Capital One’s solution comprised a journey involving three versions of the solution leading up to their current version, as well as planning and executing on their disaster recovery plan.
By solving the high data retention and DR solution instability problems, Capital One has achieved a much more manageable ecosystem and maintained the visibility needed to solve its business challenges. Powered by InfluxDB Enterprise and AWS, Capital One’s solution is providing reliable disaster recovery while delivering better service and uptime for their customers.
“InfluxDB is high-speed read and write database. So think of it. The data is being written in real-time, you can read in real-time, and when you’re reading it, you can apply your machine learning model. So, in real-time, you can forecast, and you can detect anomalies.”
Rajeev Tomer, Sr. Manager of Data Engineering