A distributed platform designed for the distributed world
In an increasingly distributed world, Expanso orchestrates jobs to run where your data lives. No costly data transfers, no redundant storage. Just insights. Engineered on top of our own open-source Bacalhau project to make big data processing faster, cheaper and more secure.
Freedom from latency and networking constraints for faster results and insights
Run jobs next to the data to utilize idle compute and reduce data throughput
More Secure
Limit risky data movement, rigorously audit jobs, and scrub data on the device
Big Data
By 2025 we are projected to generate 5x more data than we do today, putting our storage needs at roughly 175 trillion gigabytes.
These repositories of information contain critical information that can help us improve our world, but are quickly becoming too big and too decentralized to process. 57% of data is already generated outside of traditional data centers
We bring compute to where the data is.
Big Data Challenges
Problems that everyone faces when working with big data.
It’s hard to centralize
Unlike traditional data, which is generated and stored in a single location, big data is often generated across thousands of devices and processed in many different places.
It’s hard to use
The ability to adjust to larger or smaller influxes of data is necessary for cost and energy efficiency.

It’s hard to manage
With all of these inputs, it can be difficult to keep track of what's going on. You need to monitor the health of your data and jobs and ensure that everything is running smoothly.
It's hard to store
Most devices in the world can store the data they generate. However, when generating sets of data on hundreds or thousands of devices, the problem becomes  more complicated.
It's hard to secure
Moving data can be risky due to security regulations, but keeping the data in place while simplifying processing and sharing can reduce the chance of costly errors.
Expanso is a distributed platform offering for commercial and enterprise solutions.
Fast job processing
Jobs are processed where the data was created and all jobs are parallel by default, solving issues that arise with slow networks and large data transfer
Simplified IT Management
Data doesn’t need to be shared and permissioned across groups and organizations, just jobs. Configure jobs, not data, monitor job execution, and share output, preserving the privacy of datasets.
Low cost
Reduce (or eliminate) data movement costs and bandwidth needs since jobs are processed closer to the source. Take advantage of computation capabilities of idle devices, at the edge.
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