Date: 08/14/2025
Vendor: Expanso
Technology/Topic: Distributed Compute Platform for Cost Optimization and Improved Efficiency of Data Pipelines
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Welcome to the Technical Exchange Meeting (TEM)!
Expanso provides an open-source platform that brings your compute to your data, wherever it lives—cloud buckets, on-prem servers, edge devices – even warfighter’s backpacks and in-flight vehicles—so you never pay to move terabytes just to analyze them. With nearly no changes to your existing infrastructure, you define data pipelines that execute existing applications directly at each data source, even over denied and contested environments. which cut latency and egress costs in half and unlock real-time insights on large or sensitive datasets.
Built on a peer-to-peer orchestration layer, Expanso transparently schedules, distributes, and aggregates jobs across heterogeneous environments, all under your own network’s security perimeter. Because you can now ensure that raw data never leaves its origin, you maintain full control and compliance—and can spin up new processing nodes in minutes. The result is a flexible, production-ready platform that scales from prototypes to thousands of jobs per day, empowering agencies to analyze and act on their data without the traditional infrastructure overhead.
Unlike traditional data-processing platforms that require consolidating or duplicating datasets into a central cluster, Expanso “moves compute, not data,” orchestrating jobs directly at each data source—whether an S3 bucket, on-premise database, or edge device. Its peer-to-peer orchestration layer dynamically schedules and aggregates container- or script-based workloads across heterogeneous environments under your own security perimeter, preserving data sovereignty and minimizing egress and latency. Because Expanso is built on the open-source, pluggable Bacalhau, it integrates seamlessly with existing toolchains (Python, SQL, WebAssembly, GPUs) and scales from ad-hoc experiments to thousands of distributed jobs per day—unlocking real-time analytics on massive or sensitive datasets in ways that centralized or vendor-locked systems simply cannot.
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