Companies across industries continue to accumulate enormous amounts of data every day, and that’s good news, considering that 62 percent of them believe that big data has significant potential to create a competitive advantage, according to a report from PwC. Financial institutions are no different. What is critical, however, is the ability to store, access and analyze all that data so it can deliver real value. Capital One is well on its way, with a new project called the Analytics Garage that uses the agility of containers and software-defined storage to offer a big data analytics applications development platform that is scalable, fast and enables self-service.
As described in a post on the Red Hat Storage Blog, Capital One developed the Analytics Garage so developers could build, test and iterate on applications using a modular approach and users could access and evaluate data analysis tools daily. The company determined that the most cost-effective and flexible solution would be to use containerized microservices.
According the Red Hat Storage Blog post, Capital One’s Analytics Garage epitomizes the new model of provisioning, offering a buffet of microservices delivered via containers that can be used by applications or users to build more complex constructs. Rather than create individual microservice containers for each analytics tool, Capital One built an uber-container to reduce the complexity of container management while maintaining performance. The Analytics Garage is a great example of a cutting-edge analytics platform built on an open source software stack, including Red Hat Gluster Storage and Red Hat Enterprise Linux as the underlying file storage layer and operating system environment, respectively.
While containerized applications are still in their infancy in terms of full-fledged enterprise adoption (although deployment plans are growing), this is a space that is moving rapidly. Red Hat offers two compelling options for persistent containerized applications.
How is your financial organization using big data analytics? Or containers? Let us know in the comments below.