Guillermo Antonio Alvarado Mejia

Participated in the design, deployment, and support of a cloud-native environment based on Red Hat OpenStack and Red Hat Openshift for a Bank in Mexico that was about to launch a series of capabilities to integrate their digital banking services directly into the products of other non-bank partners.

With DevOps tools and automation capabilities, we orchestrated containerized banking modules that made service uptime much easier to achieve, from monitoring to alerting to remediation, enabling faster development of new products and easier maintenance of existing deployments.

The main goal was to provide the data science team with an environment to access, analyze, monitor, and visualize data. The solution was designing and supporting a data science platform deployed on Kubernetes.

The solution is based on JupyterHub, integrating it with a Kubernetes engine that allows for a robust and scalable version of JupyterHub. It gives the ability to deploy containerized environments while not being tied to any one cloud provider and giving users access to computing environments and resources.

Our team was responsible for meeting any user requests like installing packages and the use of new features and tools like MongoDB, MySQL, Spark, and Gitlab.

The business challenge was to migrate the servers hosting of 3-tier applications from on-premises to AWS Cloud in a short span of four weeks​.

I led the team that assessed the current situation and performed an application inventory to know the overall complexity. We decided what to re-host or re-platform, and after that, we designed a solution using the Well-Architected Framework.

We planned and executed a proof of concept in the cloud by testing out a subset of functions in a risk-free sandbox to gain confidence in the migration. Then we migrated the workloads without serving customer traffic to gradually start serving minimal traffic. We implemented telemetry and instrumentation and finally decommissioned the legacy solution.