Event architecture
Define event ownership, schemas, keys, ordering, retention and replay before selecting brokers or stream processors, so consumers share a stable event model.
Data & Analytics
Real-Time, Streaming & Event Data
Gromnii designs event-driven data systems for situations where delayed information loses value.
Sources
Ingest
Quality
Platform
Insight
Action
When this is useful
Use real-time, streaming and event data when applications or operations need to react to changes faster than batch pipelines can deliver them. Event architecture needs clear ordering, duplicate handling, schema evolution, replay and recovery behavior.
How information moves through the system
This reference shows one possible Real-Time, Streaming and Event Data arrangement. The actual design depends on the systems, constraints and controls involved.
01Events
02Ingestion
03Stream processing
04State
05Action
06Replay
What Gromnii builds
Streaming ingestion
Ingest continuous data with source timestamps, deduplication, back-pressure handling and replay support so downstream processing can recover from interruptions.
State & window computation
Define source ownership, schema and event meaning before implementing State and window computation.
Alerts & actions
Define how alerts and actions handle late, duplicated and out-of-order records.
Replay & ordering
Set freshness and quality checks for Replay and ordering according to the decisions it supports.
What matters in production
Ordering
Define ordering in terms of data ownership, quality, lineage and serving expectations so downstream users can see when the platform is outside acceptable limits.
Duplication
Use event IDs, idempotent consumers or transactional patterns where duplicates would create incorrect business state, and document where at-least-once delivery is acceptable.
Latency
Set an end-to-end event-delay target for each consumer and measure lag through ingestion, processing and delivery rather than only at the broker.
Recovery
Preserve offsets, replay paths and idempotent processing so event consumers can recover from failure without silently losing or duplicating business events.
What it can improve
Lower decision latency
Deliver events to applications, alerts or analytics quickly enough for the receiving process to act while the information is still useful.
More resilient event processing
Handle retries, duplicates, late events and replay explicitly so failures do not silently corrupt downstream state.
Cleaner system decoupling
Use events where producers and consumers need independent change and scaling without tight synchronous dependencies.
Related solutions and industries
Discuss a Project
Describe what Real-Time, Streaming and Event Data should change, the systems it must work with and the constraints that matter.