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Our platform tunes the infrastructure parameters and Spark configurations dynamically and continuously for each of your workloads, making your applications 2x faster and more stable on average. We make using Spark as easy as it should be.
Notebooks and scheduled jobs are triggered and autoscale in seconds to react to the desired load. No more waiting 10 minutes for a cluster to spin up!
Bring the DevOps best practices to your data stack. Kubernetes-native, our platform is available on any cloud provider and integrates with the tools you already use and love. Just point your notebooks solution to our gateway and you’re ready to go.
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.
Provide us with permissions scoped to the desired cloud account and region and our service will deploy the platform in minutes. We manage the Kubernetes cluster and deployment for you, all you need to do is point your notebooks and jobs to our gateway and you're ready to go.
If you want the flexibility to install the platform yourself, it can also be installed on an existing Kubernetes cluster through a Helm chart.
Data Mechanics charges a pay-as-you-go fee based on the total computation time spent in Spark applications, billed monthly, without commitment. This fee is additional to your cloud provider costs.
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.
Our platform is available on Google Cloud Platform GKE, Amazon Web Services EKS, Azure AKS, and your own Kubernetes setup.
We currently focus on Spark, but our vision is to expand beyond Spark and make the best data science and data processing technologies available on our containerized data platform. Let us know which tech you're interested in.