Real-time IoT analytics

Real-time IoT analytics is processing data streams from devices as they arrive: reception via a broker/stream (MQTT, Kafka etc.), aggregation, metric computation, anomaly detection, thresholds and alerts, sometimes automatic reactions (e.g. a command to a device when a threshold is exceeded). The goal is to see and react to what is happening quickly, not after the fact. Honestly about 'real time is limited by connectivity', this is key: 'in real time' in practice means 'as soon as data arrived'. IoT devices are not always online (network, power, interference, mobile connection), so data can lag, arrive in batches after reconnection, or be lost. Analytics must account for this (handling delays, gaps, duplicates), and promising instant reaction with unreliable connectivity is dishonest. We build in the reality of connectivity. Honestly about sensor accuracy, this is critical: analytics is only as good as the accuracy of the source data. Sensors have error, require calibration and drift over time; a faulty sensor gives false readings. Analytics processes what was measured but does not turn it into absolute truth. Decisions and alerts based on data must be built accounting for error (filtering, validity checking) rather than blindly trusting every figure. We honestly build this in. Honestly about infrastructure and cost: real streaming analytics at large volumes is serious infrastructure (streaming, storage, processing) that must be deployed, scaled and maintained. This is real cost. For small data volumes streaming processing is excessive — ordinary (batch) analytics or a simple dashboard (1091) is often enough. We honestly assess whether real-time is needed. Honestly about 'not a panacea': analytics shows and helps react but does not eliminate problems by itself and does not guarantee that you make the right decisions — it is a tool whose quality depends on data and configuration. Honestly about the effect: for suitable tasks it gives fast visibility and reaction to device data streams, but with honest accounting for connectivity delays, sensor error and infrastructure cost. Honestly about access: real data streams and a need for reaction speed are required. An important boundary: this is streaming analytics; dashboard — 1091; device integration — 1092; edge processing — 1098; predictive maintenance — 1100. The base price starts from 120,000 ₽ (depends on stream volume and metrics).

• You need to see and react to device data quickly, not after the fact.
• Device data streams are not processed into metrics/alerts.
• An expectation of instant reaction with unreliable device connectivity (unrealistic).
• Sensor error and the cost of streaming infrastructure are not accounted for.

• Reception and processing of device data streams (broker/stream: MQTT, Kafka etc.)
• Metrics, anomaly detection, thresholds, alerts, on-the-fly reactions
• Handling delays, gaps and duplicates (the reality of connectivity)
• Filtering and validity checking of data (accounting for sensor error)
• An honest assessment: is real-time needed or is ordinary analytics/a dashboard enough
• Honest boundaries (real time is limited by connectivity; accuracy depends on sensors; infrastructure = cost; not a panacea)
• Documentation and handover
• Review with you

• Fast visibility and reaction to device data streams (where justified)
• Metrics, anomalies and alerts accounting for data reliability
• An honest assessment: for small volumes ordinary analytics is simpler
• Honest boundaries (connectivity delays; sensor error; infrastructure cost)

• We assess stream volumes and the need for real-time (vs ordinary analytics)
• We build reception, processing, metrics and alerts with delay handling and data checking
• We honestly set boundaries (connectivity, accuracy, cost) and hand over to you

• Will analytics react instantly in real time?

  With a caveat, honestly: 'real time' = 'as soon as data arrived'. IoT devices are not always online (network, power, interference), so data can lag, arrive in batches after reconnection, or be lost. We build handling of delays, gaps and duplicates, but promising instant reaction with unreliable connectivity is dishonest. We build in the reality of connectivity rather than perfect instantaneity.

• Does analytics guarantee accuracy and correct conclusions?

  No, honestly: analytics is only as good as the accuracy of the source data. Sensors have error, calibration and drift; a faulty sensor gives false readings. Analytics processes what was measured but does not turn it into absolute truth. We build in filtering and validity checking, but decisions accounting for error are made by you. You cannot blindly trust every figure — we are honest about this.

• Do I need streaming real-time analytics specifically?

  Not always, honestly: real streaming analytics at large volumes is serious infrastructure (streaming, storage, processing) with real deployment and maintenance cost. For small data volumes this is excessive — ordinary (batch) analytics or a simple dashboard (1091) is often enough. We will honestly assess volumes and the need for speed rather than build heavy real-time where it will not pay off.