Data Mechanics joins Spot by NetApp and becomes Ocean for Apache Spark

Various logos in a circle: Data Mechanics, Airflow, IDEs, Docker, Kubernetes


Bring Your Own Tools

Enjoy a faster and more reliable development workflow with Docker. Use our pre-built optimized images, or build your own custom images to package your dependencies. Your apps will start in seconds!

Use Spark interactively by connecting Jupyter notebooks, or submit applications programmatically through our REST API or our Airflow connector.

Transparent & Flexible

The Power of Kubernetes, Without the Complexity

We're deployed on a managed Kubernetes cluster in your cloud account and in your virtual private cloud. Your sensitive data does not leave this environment. You're in control.

 We handle the complexity of Kubernetes and provide you with an easy-to-use monitoring dashboard where you can track your application's logs, metrics, and costs over time.

Data Mechanics Architecture: Dockerized Spark applications running on a Kubernetes cluster.
An illustration with a laptop and a dashboard showing a Spark application metrics over time.


50-75% Cost Reductions
From Smart Automations

We dynamically scale your applications and Kubernetes nodes based on load. We automatically tune your configurations (type of instance and disks, container memory/cpu allocations, and Spark configurations) based on the historical runs of your Spark pipelines.

Our pricing gives us an incentive to make your data infrastructure as effective as possible to reduce your cloud provider's costs. We've achieved 50 to 75% cost reductions for customers migrating from competing platforms like Databricks or EMR.

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