Modern data centers operate across compute, power, cooling, and networking systems-each producing telemetry in different formats and structures. KŌJŌ Stack normalizes infrastructure data across all systems, establishing a consistent foundation for downstream consumption. Owning the first mile ensures that infrastructure telemetry is consistent, reliable, and usable across all systems.
Structured at the Source
Compute nodes, power distribution units, cooling systems, and network equipment each produce telemetry in vendor-specific formats with incompatible addressing. No unified data model exists across infrastructure layers, forcing every downstream consumer to build its own normalization.
Multi-site and hybrid environments compound fragmentation. The same type of equipment at different locations may report different metrics in different formats. Fleet-wide analysis requires extensive per-site reconciliation.
Every system that consumes infrastructure telemetry-capacity planning, automation controllers, analytics platforms-must independently handle format translation, quality assessment, and context reconstruction. Integration complexity scales linearly with every new data source.
Without structured, prepared data at the first mile, downstream systems inherit every inconsistency, gap, and limitation of the raw source data.
When compute, power, cooling, and networking telemetry live in vendor-specific formats with incompatible addressing, correlating a cooling anomaly with a compute density change or a power event requires manual investigation across separate monitoring tools. Automated capacity planning operates on incomplete data.
The same type of equipment at different data centers may report different metrics in different formats. Fleet-wide analysis-comparing PUE across sites, tracking capacity trends, or benchmarking performance-requires extensive per-site reconciliation that scales linearly with the number of locations.
Every automation system that consumes infrastructure telemetry must independently handle format translation, quality assessment, and context reconstruction. Each new data source or automation consumer adds another integration point, creating a fragile mesh of custom adapters that resists change.
Compute, power, cooling, and networking telemetry is ingested directly from heterogeneous systems and normalized into a consistent structure. Vendor-specific formats are resolved at the point of ingestion-downstream systems consume from a single canonical model.
Event-driven pipelines deliver infrastructure telemetry with predictable, low-latency behavior. Consistent timing and ordering across distributed environments ensure that downstream systems-automation controllers, capacity planners, analytics-receive reliable inputs.
Data processing occurs near the infrastructure it describes. RBE filtering reduces telemetry volume at the source. CEL expressions compute derived metrics and aggregates before data traverses the network-reducing latency and bandwidth requirements. Structured telemetry routes to InfluxDB or TimescaleDB for time-series historian, S3 for long-term storage, or Kafka for event-driven consumers.
All infrastructure telemetry maps into a unified hierarchical model. Compute, power, cooling, and networking data follows a consistent addressing structure, enabling cross-system correlation and simplified downstream consumption without per-source adapters.
Modern data center infrastructure generates telemetry across fundamentally different domains: compute nodes report via IPMI/Redfish with server-level granularity, power distribution units communicate over Modbus with circuit-level metering, cooling systems use BACnet with zone-level monitoring, and network equipment exposes metrics via SNMP or streaming telemetry. Each domain uses different data models, different update rates, and different quality semantics. Without normalization at the point of ingestion, constructing a cross-domain view-understanding how a compute workload change affects cooling load and power consumption-requires joining data across incompatible schemas and time bases. KŌJŌ Stack resolves this by mapping all infrastructure telemetry into a unified hierarchical model at the edge, establishing a consistent schema that downstream systems consume regardless of the source domain. This structural consistency is only possible when data is normalized at the first mile.
Standardized telemetry across distributed data centers
Clean, structured data eliminates per-consumer translation
Same data model deploys to every new environment
“Infrastructure telemetry from compute, power, and cooling now follows a single structure across all our sites. Automation systems consume clean, consistent data without custom integration per environment.”
Owning the first mile ensures data centers data is consistent, contextualized, and usable across the enterprise.
