Columnar object storage
Write Parquet to S3, GCS, Azure, or MinIO, with Postgres tracking organizations, streams, schemas, and files.
Keep logs, metrics, traces, and continuous profiles under one tenant and time model. Columnar telemetry shares one query plane while lossless pprof archives stay portable on object storage.

Each signal keeps an operator-friendly view while tenant, time, object storage, and investigation context stay common.
Write Parquet to S3, GCS, Azure, or MinIO, with Postgres tracking organizations, streams, schemas, and files.
Use joins, CTEs, and window functions through DataFusion when an incident does not fit a single telemetry type.
Logs, PromQL metrics, trace waterfalls, profiling flame graphs, service maps, and dashboards remain optimized for their own job.
Organization filters are rewritten into query plans rather than delegated to naming conventions.
workflow
The data plane carries the information the operator would otherwise reconstruct by hand.
OTLP, Prometheus, Loki, Elasticsearch bulk, Syslog, pprof, Pyroscope, and cloud drains enter one governed data plane.
WAL and Arrow buffers flush into Parquet, with Tantivy and metadata supporting selective reads.
Choose the surface that fits the signal without moving data to another backend.
Move between signal views with the constraints of the investigation intact.
proof / shipped
The public architecture and API expose the same cross-signal model described here.
Keep logs, metrics, traces, and continuous profiles under one tenant and time model. Columnar telemetry shares one query plane while lossless pprof archives stay portable on object storage.