I’m a Staff Platform Engineer at Empathy.co. I mostly manage Kubernetes Clusters, CI/CD orchestration, Elasticsearch, MongoDB and try to break things on AWS, GCP and Azure.
This blog post is a continuation of Cloud FinOps - Part 3: Cloud Cost Report, in which we will explore how to gain cost visibility on Kubernetes Workloads. MotivationNowadays, a lot of companies run their workloads on Kubernetes because there are many benefits to choosing Kubernetes as the main solutions
In the first post of this four-part series, we explored the Cloud FinOps principles and the key milestones in the journey to start walking in FinOps: Tag Allocation StrategyCost ReportUsage ReportIn this post, we’ll dig deeper into a crucial step in the Inform phase of FinOps culture: creating a
FinOps, short for Cloud Financial Operations, helps teams optimise cloud costs to get the most value for their projects. Teams adopt best practices and a collaborative culture to manage cloud operations with greater financial accountability: balancing cost, speed and quality. When your teams have problems managing their cloud costs, following
MotivationThis article is an overview of the path we followed to migrate Spark Workloads to Kubernetes and to avoid EMR dependency. EMR was an important support tool at Empathy.co to orchestrate Spark workloads, but once the workloads became more complex, the use of EMR also became more complicated. So,
Using Spark on K8s to overcome dependency on cloud providersFor the last few weeks, I’ve been deploying a Spark cluster on Kubernetes (K8s). I want to share the challenges, architecture and solution details I’ve discovered with you. ChallengesAt Empathy, all code running in production must be cloud-agnostic. As