Event-driven distributed logs, Change Data Capture (Debezium), stream processing, and high-throughput real-time pipelines.
Stop switching between helpdesk, billing, and analytics tabs. Build a unified customer timeline with a canonical event model, identity resolution, idempotent Kafka ingestion, and a partitioned PostgreSQL feed.
Eliminate machine learning train/serve skew and data leakage. Architect low-latency feature stores bridging Apache Kafka event streams, Feast point-in-time registries, and Redis Cluster for sub-3ms online inference.
Sub-second analytical queries across 1 billion rows: MergeTree sparse indexing, vectorized SIMD execution, Hydra PostgreSQL columnar extensions, and Kafka ingestion.
Eliminate the dual-write problem in distributed microservices: Atomic database commits, PostgreSQL WAL change data capture, Debezium Outbox Event Router SMT, and idempotent consumers.
Why traditional month-end churn metrics arrive 60 days too late: architecting a real-time predictive retention engine using ClickHouse sliding feature windows, exponential decay weights, Cox Proportional Hazards modeling, and automated Customer Success sentinel workflows.
Moving beyond passive 3D dashboards: how bidirectional OT/IT event bridges with OPC UA and Apache Kafka reduce unplanned industrial downtime by up to 28%.
Our engineering team designs sub-50ms distributed backends, edge AI pipelines, and high-concurrency systems.