The simplest way
to run Spark

Just send your code, we handle the mechanics.

Want to learn more? Data Mechanics is in private beta but we’re working hard to make it available to everyone. Enter your information below and we’ll reach out to you.

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Open-Source and Kubernetes native

Bring the DevOps best practices to your data stack. We deploy Spark on Kubernetes and natively integrate with the best-in-class open source tools from the Kubernetes and Data Science ecosystem.
Just point your notebooks to our gateway and you’re good to go.

Maintenance is on us

By automatically tuning infrastructure and Spark configurations dynamically and continuously for each of your workloads, our platform makes your applications 2x as fast and stable. We make using Spark as easy as it should be.

The fastest experience with Spark

Notebooks and scheduled jobs are triggered in seconds thanks to containerization and Kubernetes autoscaling. No more waiting 10 minutes for a cluster to spin up!

Frequently Asked Questions

How do I use it?

Run autoscaled Jupyter kernel with Spark support from the notebook environment you already have. Use our operator library to launch scheduled jobs from your favorite orchestrator (Airflow, Luigi, Azkaban, custom schedulers).

We automatically set the infrastructure parameters and Spark configurations so you don’t need to worry about them.

What’s the installation process like?

We provide a Kubernetes manifest packaged as a Helm chart containing all of Data Mechanics.
Deploy it on your Kubernetes cluster, point your Jupyter notebook server to our gateway, and the Data Mechanics platform is running.

How much does it cost?

Data Mechanics charges a pay-as-you-go fee based on the total computation time spent in Spark applications. This fee comes in addition to the infrastructure cost that you already bear.

All the competing data platforms base their fee on the total server uptime, whether these servers are actually used by Spark or whether they are sitting idle. As a result, these platforms have no incentive to reduce the wasted idle time, and up to 80% of your monthly bill typically comes from these wastes.

At Data Mechanics, we don’t charge you on this idle time so that we’re incentivized to make your data infrastructure as performant and cost-effective as possible.

Which infrastructures do you support?

Our platform is deployed on any Kubernetes cluster, including Google Cloud Platform GKE, Amazon Web Services EKS, Azure AKS, and your own setup.

Do you support other big data technologies than Spark?

We currently focus on Spark, but other technologies will be next.Let us know which one you’re interested in.

The hassle-free Spark platform deployed on Kubernetes
Contact us for a demo or to learn more about Data Mechanics!
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