Francis is pleased with the results and says, "We're going to use this infrastructure for lots of other application workloads as time goes on. And the team hit the goal within 100 days.
#Blackrock aladin install
"Our goal was: How do you give people tools rapidly without having to install them on their desktop?" says Francis. It's not so much that we had to solve our main core production problem, it's how do we extend that? How do we evolve?" SolutionÄrawing from what they learned during a pilot done last year using Docker environments, Francis put together a cross-sectional team of 20 to build an investor research web app using Kubernetes with the goal of getting it into production within one quarter. Being able to spin that up on demand, tear it down, make that much more dynamic, became a critical thought process for us. We have existing environments that do these things, but we needed to make it real, expansive and scalable. "Managing complex Python installations on users' desktops is really hard because everyone ends up with slightly different environments. "We want to be able to give every investor access to data science, meaning Python notebooks, or even something much more advanced, like a MapReduce engine based on Spark," says Michael Francis, a Managing Director in BlackRock's Product Group, which runs the company's investment management platform. But in their data science division, there was a need for more dynamic access to resources. The world's largest asset manager, BlackRock operates a very controlled static deployment scheme, which has allowed for scalability over the years.