Best-in-Class Event Streaming
for Agents, Apps, and Analytics
for Agents, Apps, and Analytics
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RisingWave ingests high-volume sensor streams, detects anomalies in sub-second windows, and powers live dashboards — all with standard SQL and no Flink pipelines.
From sensor reading to alert in under 100 milliseconds
Define complex windows, joins, and aggregations in plain SQL
Horizontally scale compute without scaling storage
Store historical sensor data on S3 at object-storage cost
IoT generates a firehose of time-series data. Most teams wire together Kafka, Spark Streaming or Flink, a time-series DB, and a separate alerting layer. Each hop adds latency, operational burden, and failure points.
Hourly or minute-level aggregations cannot detect a temperature spike or equipment failure the moment it happens.
Managing Flink jobs, schema registries, and stateful operators requires specialized expertise and breaks during upgrades.
Storing every raw sensor reading in a traditional database or data warehouse becomes cost-prohibitive at scale.
RisingWave replaces the Kafka Consumer + Flink + Time-Series DB stack with a single SQL layer that handles ingestion, processing, and serving.
| Capability | Kafka + Flink + TSDB | RisingWave |
|---|---|---|
| Anomaly Detection Latency | 500ms - 2s | < 100ms |
| Query Language | Java / Scala DSL | Standard SQL |
| State Management | Flink state backend | Built-in, S3-backed |
| Operational Complexity | High (3+ systems) | Low (single system) |
| Time-Series Windowing | Manual window operators | SQL TUMBLE / HOP / SESSION |
| Historical Backfill | Separate batch job | Unified streaming + batch |
| Infrastructure Cost | High | Low (disaggregated storage) |
From factory floors to connected vehicles, RisingWave handles every IoT streaming pattern.
Compute rolling vibration, temperature, and pressure averages over sliding windows. Trigger alerts before equipment failure causes downtime.
CREATE MATERIALIZED VIEW machine_alerts AS SELECT device_id, AVG(temperature) AS avg_temp FROM sensor_readings GROUP BY device_id, TUMBLE(event_time, INTERVAL '1' MINUTE) HAVING AVG(temperature) > 85;
Process GPS, speed, and engine diagnostics from millions of vehicles. Power real-time fleet dashboards and driver safety scoring.
CREATE MATERIALIZED VIEW fleet_summary AS
SELECT vehicle_id,
LAST_VALUE(speed) AS current_speed,
AVG(fuel_level) OVER (PARTITION BY vehicle_id
ORDER BY event_time ROWS 10 PRECEDING) AS avg_fuel
FROM vehicle_telemetry;Aggregate smart meter readings across thousands of grid nodes. Detect overloads and balance load distribution in real time.
CREATE MATERIALIZED VIEW grid_load AS SELECT grid_zone, SUM(power_kw) AS total_load, MAX(power_kw) AS peak_load FROM smart_meters GROUP BY grid_zone, TUMBLE(event_time, INTERVAL '30' SECOND);
Stream data from vision systems and sensors on assembly lines. Flag defects in real time to halt production before entire batches are affected.
CREATE MATERIALIZED VIEW defect_alerts AS SELECT line_id, product_id, defect_type, confidence FROM quality_events WHERE confidence > 0.95 AND event_time > NOW() - INTERVAL '5' SECOND;
A minimal, production-grade IoT streaming pipeline using RisingWave.
The best architecture ingests sensor data via Kafka or MQTT, processes it in a streaming database like RisingWave using SQL, and writes results to a dashboard or downstream sink. This replaces complex Flink/Spark pipelines with a single SQL layer.
Use a horizontally scalable streaming database that decouples compute from storage. RisingWave stores state on S3 and scales compute nodes independently, processing millions of events per second without data loss.
Batch processing aggregates IoT data in periodic windows (minutes to hours), missing anomalies between runs. Streaming processes each reading as it arrives, enabling immediate anomaly detection, predictive maintenance, and real-time dashboards.
Define a materialized view in RisingWave that computes rolling averages over a sliding window. When a sensor reading deviates beyond a threshold, trigger an alert via a sink to Kafka, PagerDuty, or a webhook.
Yes. For MQTT, route messages through an MQTT-to-Kafka bridge such as EMQX and ingest from Kafka into RisingWave. RisingWave natively supports Kafka, Redpanda, Pulsar, and Kinesis.
RisingWave is open source and free to use. Connect your Kafka topic, write a SQL materialized view, and see real-time sensor analytics in minutes.