Distributed Compute Over Data
Sanitize and process application logs at source before centralizing, resulting in reduced transport costs, quicker insights, and strict data privacy compliance.
Distributed ML training across remote devices without central data aggregation, leading to enhanced security, lower latency, and maintained model accuracy.
Virtual data warehouse with federated queries across distributed sources, enabling faster insights, cost savings, and access to real-time data.
Query device fleets instantly using Bacalhau’s OSQuery with a SQL-like engine, all without data centralization. Container support to swiftly audit, configure, and monitor health. Benefit from enhanced uptime, faster issue resolution, and reduced engineering effort.
Distributed Files Data processing across distributed storage and varied regions, resulting in significant cost savings, faster data processing, and minimized compliance risks.
Decentralized job coordination for resilient execution across unstable networks, ensuring reliable job execution, fewer failures, and support for geo-distributed queues.
Jointly train ML models without sharing raw data across divisions. Enjoy superior model accuracy and minimized regulatory risks.
Coordinate experiments across shared computing resources without transferring data. Benefit from optimal hardware usage, accelerated research, and uncompromised data security.
