Timely operational views
Make relevant events available within an agreed latency objective, with visibility into delays.
EVENT-DRIVEN DATA SYSTEMS
Stream Data Processing
Turn continuous events into timely decisions.
Connect live application, transaction, and device events to dependable processing pipelines designed around your latency, scale, and recovery requirements.
THE OPPORTUNITY
Build for the pace of your business.
Some decisions cannot wait for the next scheduled load. Product activity, equipment telemetry, transaction updates, and operational alerts require information to move while the business is running. GKAICORE designs streaming systems that address both the flow of events and the operational complexity behind it: delayed messages, duplicate delivery, changing schemas, downstream outages, and uneven traffic.
We begin by identifying the decision the stream supports and the latency it actually requires. From there, we define ingestion, processing, state management, and delivery patterns that fit your environment. Streaming can complement existing batch workloads, enabling timely operational views while preserving historical data for reconciliation and deeper analysis.
Controlled failure handling
Keep replay, retries, invalid events, and downstream interruption within a documented operating model.
Room for changing demand
Design partitioning and capacity around traffic patterns and measurable scaling requirements.
SERVICE CAPABILITIES
Built around the complete operating requirement.
A focused set of capabilities, tailored to your sources, systems, and business priorities.
Event ingestion & connectivity
Connect applications, message brokers, device platforms, or change-data feeds. Define event ownership, source authentication, partitioning, retention, and the contracts producers and consumers must follow.
Real-time transformations
Filter, enrich, route, and aggregate events as they arrive. Select stateless or stateful processing patterns based on the business rule, required context, and the cost of maintaining processing state.
Event-time & windowing logic
Handle the difference between when an event occurred and when it was received. Define windows, late-arrival policies, ordering assumptions, and correction behavior for meaningful operational calculations.
Delivery & data quality controls
Detect malformed events, incompatible schema changes, and duplicate records. Define quarantine, deduplication, and validation rules according to source guarantees and downstream system capabilities.
Replay & state recovery
Plan retention, offsets, checkpoints, and replay boundaries. Test how consumers recover after interruption and how historical events can be reprocessed without creating unintended downstream effects.
Lag, throughput & capacity monitoring
Observe processing lag, failed events, throughput, and consumer health. Evaluate backpressure and scaling behavior under representative traffic, with alerts tied to operational impact.
WHERE IT FITS
Practical applications for your business.
Device & equipment telemetry
Process sensor readings, enrich them with asset context, and prepare data for operational monitoring.
Product & customer events
Capture application activity and build timely engagement, usage, or service-health views.
Transaction & workflow updates
Move authorized transaction or workflow events between systems to support timely reconciliation and decision support.
OUR DELIVERY APPROACH
A clear path from requirements to operation.
Define event requirements
Agree event contracts, expected traffic, latency objectives, ordering needs, and retention boundaries.
Design the flow
Choose ingestion, processing, state, delivery, and recovery patterns that fit the consuming systems.
Validate under load
Test duplicates, late events, traffic bursts, producer changes, and downstream outages.
Operationalize
Configure monitoring, alerts, deployment procedures, and replay runbooks before production handover.
WHAT YOU RECEIVE
A solution your team can understand and operate.
Deliverables are confirmed in the engagement scope and reviewed against agreed acceptance criteria.
- Event contracts and flow architecture
- Stream processing implementation
- Schema and invalid-event handling
- Checkpoint and replay configuration
- Latency and throughput monitoring
- Load-test findings and operational runbooks
COMMON QUESTIONS
Before we get started.
Do all of our workloads need streaming?
No. Streaming is useful when the decision or workflow benefits from timely updates. Scheduled reporting and large reconciliations may remain better suited to batch processing. We can design a combined approach that avoids adding operational complexity where it provides little value.
How do you handle duplicate or out-of-order events?
We assess event identifiers, timestamps, ordering requirements, and source guarantees. The implementation may use deduplication, event-time windows, or downstream correction rules. These choices are documented and tested against the behavior expected by consumers.
What if a destination becomes unavailable?
The design considers buffering, retry limits, retention, backpressure, and recovery. We establish how long events can be retained and what operators should do before retention or capacity limits are reached. The exact approach depends on the broker and destination.
Can streaming feed our existing analytics platform?
Yes. We can prepare streaming outputs for your warehouse, lake, application, or dashboard layer. The target's ingestion capabilities and refresh behavior influence the end-to-end latency you can achieve.
CONNECTED CAPABILITIES
Build on the next part of your data journey.
LET'S DEFINE THE NEXT STEP
Start a Conversation Make your next event actionable.
Tell us where delayed data limits your operations. We will help define a streaming approach with clear business and technical objectives.