Aerospace & Satellite
Full-Lifecycle Quality Monitoring and Assessment
A unified data platform that enables data-driven decisions, full lifecycle quality traceability, and intelligent fault prediction for satellite systems.
Request DemoFrom Manufacturing to Launch
Satellite development spans dozens of subsystems — structural, thermal, power, and propulsion — continuously generating high-frequency, high-dimensional time-series data across long manufacturing cycles and rigorous ground testing, culminating in real-time monitoring demands at launch.
The industry is moving beyond fragmented, phase-based data management toward unified platforms that improve reliability, reduce development costs, and accelerate iteration cycles.
DolphinDB Solution
High-Performance Data Foundation
- Supports 100 million data points/second write throughput at 1.8GB/s, with read throughput exceeding 100 million points/second — up to 100x faster than traditional architectures.
- Nanosecond timestamp precision for high-accuracy event recording, with Decimal64/128 support for scientific computation.
- Advanced compression algorithms (lz4, delta-of-delta, zstd, chimp) reduce disk usage to a fraction of traditional databases, significantly lowering storage costs.
- Flexible partitioning by time range, hash, and composite strategies improves query efficiency; single tables support up to 32,768 columns to accommodate tens of thousands of satellite measurement points.
Unified Streaming and Batch Processing
- The same codebase applies to real-time monitoring and historical analysis, accelerating development and iteration cycles.
- Two engines — reactive state and stateless engine — abstract complex logic into configurable tables, enabling low-code industrial event monitoring.
- High-fidelity historical data replay with millisecond-precision time alignment supports fault traceability and simulation across manufacturing and launch phases.
Advanced Analytics and Machine Learning
- Built-in functions enable real-time correlation matrix computation across thousands of measurement points, anomaly pattern detection, and signal processing (FFT, wavelet transforms) for frequency-domain analysis of test waveforms.
- Machine learning workflows run natively within the database. Engineers can call random forest, SVM, and other algorithms via SQL-like syntax to build predictive models for key performance indicators such as component fatigue life and assembly pass rates. Trained models deploy seamlessly as real-time inference functions within the stream processing engine.
Key Benefits
Closed-Loop Data Across the Satellite Lifecycle
Unified management and correlation analysis across manufacturing, testing, and launch phases enables complete quality traceability — shifting from point-in-time monitoring to full lifecycle visibility.
Real-Time and Predictive Quality Monitoring
Quality monitoring shifts from post-hoc analysis to real-time detection, enabling anomalies to be identified and assessed during testing or launch — significantly reducing the time between issue detection and resolution.
Reusable Engineering Knowledge
A single platform for data governance progressively transforms engineering experience into verifiable, inheritable data assets — continuously improving product consistency and reliability.
Manage ultra-high-frequency measurement data across satellite manufacturing, ground testing, and launchEnable real-time monitoring, anomaly detection, and critical event traceability during test campaignsEfficiently manage long-cycle, multi-batch, and multi-configuration test historyTransform test data into reusable analytical models and validation capabilities that improve development efficiency and quality control