EXAMPLE USE CASES

LOG PROCESSING

Sanitize and process application logs at source before centralizing, resulting in reduced transport costs, quicker insights, and strict data privacy compliance.

EDGE ML TRAINING

Distributed ML training across remote devices without central data aggregation, leading to enhanced security, lower latency, and maintained model accuracy.

DISTRIBUTED DATA WAREHOUSE DATA

Virtual data warehouse with federated queries across distributed sources, enabling faster insights, cost savings, and access to real-time data.

DISTRIBUTED FLEET MANAGEMENT

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.

PROCESSING OVER GEOGRAPHICALLY

Distributed Files Data processing across distributed storage and varied regions, resulting in significant cost savings, faster data processing, and minimized compliance risks.

RUNNING JOBS OVER UNRELIABLE NETWORKS

Decentralized job coordination for resilient execution across unstable networks, ensuring reliable job execution, fewer failures, and support for geo-distributed queues.

FEDERATED LEARNING WITH ISOLATED DIVISIONS

Jointly train ML models without sharing raw data across divisions. Enjoy superior model accuracy and minimized regulatory risks.

SHARED MACHINES

Coordinate experiments across shared computing resources without transferring data. Benefit from optimal hardware usage, accelerated research, and uncompromised data security.

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