Platformatory
Consulting
When sub-second latency matters, copy-and-reshape analytics pipelines bottleneck. High-performance estates need a streaming backbone—and the log as the primary abstraction.
Stack
Apache Kafka
Apache Flink
Kafka Connect
Debezium
Apache Hudi
ClickHouse
Apache Druid
Kubernetes
Surfaces
How we engage. The architectural argument is below.
Rich, domain-driven, data-intensive applications built for scale and resilience—on a streaming backbone, not a batch afterthought.
Data exchange and multi-modal data products on a Kappa-shaped foundation and open table formats—one processing model, not dual pipelines forever.
Insights, reporting, and inference from large-scale data at sub-second latencies—served from streams and specialized stores, not overnight copy jobs.
CDC, event mesh, and system sync—always-on exchange across systems of record and engagement.
How we build
Conventional data architectures bottleneck when you need sub-second latencies for analytical data. Primary reason: you copy and re-layout data for analytics—then pay the eventual overheads of that copy.
Even on the operational plane, aggregating events in real time is not fundamentally a database capability. That work lives with LSM-tree primitives, actor-oriented systems, stateful stream processing, and specialized databases.
Bottom line: for high-performance systems you need a streaming backbone, and the log as the primary abstraction.
Next step
Tell us the systems of record, the consumers, and what sub-second means in your org. We will map the right starting surface.
