Real-Time Stream Processing
A production-ready accelerator for real-time stream processing — from multi-source event capture through Kappa-style medallion transformation to correctness under out-of-order delivery.
Kappa + Medallion Architecture
A single streaming pipeline handles both real-time and historical processing — there's no separate batch layer to keep in sync. Reprocessing just means replaying the log. Events flow through the same Bronze → Silver → Gold layering used in batch, so the two accelerators share a consistent mental model.
Bronze (Raw Stream)
Immutable, replayable event log capturing every message exactly as produced, retained for reprocessing and audit.
Silver (Enriched Stream)
Deduplicated, validated, and enriched events — conformed to a shared schema and joined with reference data.
Gold (Serving Stream)
Aggregated, query-ready streams and materialized views consumed directly by dashboards, APIs, and downstream services.
Real-Time Capture from Diverse Sources
Every source speaks a different protocol and shape. The accelerator provides pre-built ingestion adapters so IoT telemetry, clickstream events, and application data land in the same stream format — without a bespoke integration for every source.
IoT & Sensor Ingestion
Lightweight MQTT/CoAP-compatible collectors for high-frequency device telemetry, with edge buffering for intermittent connectivity.
Clickstream & Behavioral Events
SDK and API-based capture for web/app click and interaction events, normalized into a common event schema.
Change Data Capture
Log-based CDC connectors stream database changes into the same real-time pipeline as first-class events.
Protocol Normalization
Adapters translate MQTT, HTTP, Kafka, and custom protocols into a single canonical event envelope before entering the pipeline.
Out-of-Order Events via Watermarking
Network delays, retries, and mobile connectivity mean events rarely arrive in the order they occurred. The accelerator uses watermarking to track event-time progress and decide when a window is "complete enough" to emit — balancing correctness against latency.
Event-Time Watermarks
Tracks the progress of event time (not arrival time) per source, so windows are triggered based on when events actually happened.
Configurable Lateness Allowance
Tunable grace periods per use case, trading a small amount of latency for materially fewer dropped or misplaced events.
Late Event Handling
Events that arrive after their window closes are routed to a side output for correction, backfill, or audit rather than silently dropped.
Windowing Strategies
Tumbling, sliding, and session windows available out of the box, so aggregation logic matches the shape of each use case.