Spark on Kubernetes, The Easy Way

Spark on Kubernetes, The Easy Way

How our startup Data Mechanics (YCombinator, S19) builds on top of the open-source project. Let's start with Spark on Kubernetes, The Easy Way.

I’m an ex-Databricks engineer, now co-founder of Data Mechanics, a managed Spark platform deployed on a Kubernetes cluster inside our customers cloud account (AWS, Azure, or GCP). Our mission is to make Spark more developer friendly and more cost-effecvtive for data engineering teams.

In this story, I’m answering a legitimate question we get asked all the time — what’s the point of buying your startup offering versus running Spark on Kubernetes the open-source way. The short answer is:

  1. We add specific features that make Spark-on-Kubernetes easy-to-use, cost-effective, secure and stable.
  2. We take care of the setup and maintenance of Spark and Kubernetes for you — saving your DevOps team a lot of headaches.
  3. While keeping you in control and giving you the flexibility of a home-built solution, as we’re deployed in your cloud account and your private network.

But let’s support these points with facts and illustrations! What do we add on top of Spark-on-Kubernetes open-source?

kubernetes

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