// 01
Operational monitoring
Production lines, equipment or service operations where problems need to be visible while they're still happening.
// 04 / Real-Time Analytics & Streaming
When yesterday's data is already too late.
Real-time analytics for situations where waiting for the next scheduled refresh has an actual operational cost. Built with Microsoft Fabric or Databricks when the use case genuinely justifies it.
// Real-time isn't automatically better.
// Events to decisions
The event happened. The system should know.
A streaming architecture captures events as they arrive, processes the relevant data and makes it available for monitoring, investigation or action.
EVENT STREAM / ILLUSTRATIVE MONITOR
● EXAMPLE EVENT FLOW
01 / EVENT SOURCE
Incoming telemetry
sensor_014
82.4°C
sensor_021
79.1°C
sensor_014
94.8°C
sensor_018
81.0°C
02 / PROCESS & EVALUATE
Streaming logic
// illustrative event rule
source: "equipment_telemetry"
window: "rolling"
condition: "temperature > threshold"
action: "flag_for_review"
source: "equipment_telemetry"
window: "rolling"
condition: "temperature > threshold"
action: "flag_for_review"
03 / OPERATIONAL OUTPUT
Equipment monitor
94.8°C
Example reading above configured threshold
Status
REVIEW
Equipment
sensor_014
// Illustrative interface with fictional telemetry values. Not a live system or a claim about measured processing latency.
// Where real-time earns its complexity
Some decisions can't wait for tomorrow.
Streaming is useful when the value of acting sooner outweighs the extra engineering and operational overhead.
// 02
Fraud & anomaly detection
Transactions or activity patterns that need timely investigation instead of waiting for an overnight batch.
// 03
IoT & telemetry
Sensor readings, environmental measurements and connected assets that generate continuous streams of operational data.
Most businesses don't need real-time analytics.
A reliable hourly or daily pipeline is often enough for reporting. If the business can make the same decisions with scheduled refreshes, adding streaming infrastructure may just create more complexity and cost.
// What we can build
The streaming layer. And the boring parts around it.
Moving events quickly is only part of the job. A useful solution also needs reliable processing, sensible storage, monitoring and a clear way for people to act on the results.
01 / Ingestion
Eventstream integration
Connect supported event sources and establish a dependable path into the analytics platform.
02 / Fabric
Eventhouse & KQL
Store, query and investigate event data using Microsoft Fabric Real-Time Intelligence capabilities where appropriate.
03 / Databricks
Structured Streaming
Process incoming data with Spark Structured Streaming and integrate it into existing lakehouse engineering patterns.
04 / Analytics
Operational dashboards
Present relevant events, trends and conditions in reporting interfaces designed for operational decisions.
05 / Reliability
Monitoring & recovery
Account for late events, failures, data quality and operational visibility rather than assuming the stream will always behave perfectly.
06 / Architecture
Batch vs streaming assessment
Evaluate latency requirements, complexity, operational effort and platform cost before recommending a streaming solution.
// Our approach
First, prove that the delay matters.
Real-time is a technical capability, not a business objective.
We start with the decision that needs to happen sooner and work backwards to the data, latency and operational requirements.
If a faster scheduled pipeline solves the problem, that's worth saying before anyone commits to a streaming architecture.
01
Define the operational decision
Identify what needs to happen, who needs the information and what a delay actually costs.
02
Establish the latency requirement
Determine whether the use case needs seconds, minutes, hourly updates or something less demanding.
03
Choose the simplest viable design
Compare streaming with scheduled processing and select the architecture that meets the requirement without unnecessary overhead.
04
Build and validate the workflow
Test ingestion, processing, outputs and failure scenarios before relying on the solution operationally.
// Not every pipeline needs to stream
Start with the requirement. Then choose the architecture.
If your main problem is unreliable reporting, disconnected systems or slow scheduled refreshes, a better data engineering foundation may be the right place to start.
// Real-time or just more frequent?
How fast does your data actually need to be?
Tell us what you're monitoring and what happens when the data arrives late. We'll help assess whether real-time streaming is justified or whether a simpler approach will do the job.
Book a free call ↗
// No sales pitch. No commitment.