The Case for Unified Observability in Propulsion System Development

Introduction

In today's era of rapid technological advancement, propulsion teams across aerospace, defense, and commercial spaceflight are operating increasingly complex engine systems that generate vast amounts of high-frequency, high-cardinality telemetry data. However, legacy tools designed for industrial IoT struggle to handle the complexity and volume of propulsion test data. Developing, testing, and optimizing high-performance engines—whether for reusable rockets, hypersonic vehicles, or next-generation air-breathing propulsion—demands observability software far beyond the needs of standard industrial sensors.

To properly validate and refine these propulsion systems, engineers have often resorted to building custom in-house data review tools. While these tools may work for a single test campaign, they introduce major challenges: pulling critical engineers away from propulsion optimization, requiring constant maintenance, and ultimately failing to scale with increasing test complexity. The result is wasted time, fragmented analysis, and preventable inefficiencies in test operations.

For propulsion teams to iterate quickly, improve efficiency, and maintain safety, a new approach to observability is needed. This paper explores why propulsion engineers must move beyond traditional monitoring, how AI-powered observability transforms real-time analysis, and how Sift provides a fully integrated, scalable solution tailored to the needs of engine development and testing.

Observability 101: More Than Just Monitoring

Understanding why comprehensive observability is a game-changer for propulsion engineers begins with recognizing the limitations of traditional monitoring tools. Many existing solutions require engineers to define fixed thresholds for expected anomalies, forcing teams to preemptively predict every potential failure mode.

While these tools may work for a single test campaign, they introduce major challenges: pulling critical engineers away from propulsion optimization, requiring constant maintenance, and ultimately failing to scale with increasing test complexity.

At its core, observability refers to the tools engineers use to analyze and interpret propulsion system behavior at every stage—from initial component validation to full-duration hot fires. As engines grow in complexity, effective telemetry ingestion and analysis become mission-critical. Observability platforms empower engineers to:

• Detect and diagnose anomalies in high-speed turbomachinery and combustion performance.
• Analyze real-time pressure, temperature, and vibration trends across thousands of sensors.
• Identify early indicators of injector malfunctions, combustion instabilities, or structural fatigue.
• Compare historical test data to rapidly validate new design iterations.

Without an advanced observability stack, propulsion engineers are forced to sift through massive volumes of raw data manually—an approach that simply cannot scale for modern test programs.

Beyond Basic Monitoring: The Power of Observability

Many monitoring tools on the market fall short of true observability. Platforms like Grafana rely on preconfigured dashboards and manual alerting thresholds, limiting their ability to surface unknown unknowns.

For propulsion teams, where test conditions are highly dynamic and real-world scenarios cannot always be anticipated in advance, this approach is insufficient. True observability enables engineers to:

• Automate anomaly detection for transient combustion instabilities.
• Correlate fuel flow irregularities with chamber pressure fluctuations in real-time.
• Surface unexpected trends in oxidizer mixture ratios before they cause a hard start or flameout.
• Leverage logs, metrics, and traces to understand engine behavior under varying test conditions.

Rather than relying on rigid monitoring systems, modern propulsion teams need observability platforms that adapt to evolving test scenarios and provide deep insight into system behavior.

The Observability Challenge: Why Propulsion Teams Aren't Using It

Given its advantages, why haven’t all propulsion teams fully adopted observability? The reality is that integrating observability into complex test programs is difficult. Many organizations attempt to develop their own fragmented solutions, requiring dedicated teams to build and maintain telemetry ingestion, storage, and analysis pipelines.

In application software and IT, observability has already become standard practice. The data proves its value:

• 90% of IT professionals believe observability is important and strategic to their business, but only 26% said their observability practice was mature. 50% are currently implementing observability (New Relic).
• 91% of IT decision makers see observability as critical at every stage of the software lifecycle, citing the biggest benefits to planning and operations (New Relic).
• 92% of surveyed engineers believe observability tools enable more effective decision-making (Tanzu VMware).
• Advanced observability deployments can cut downtime costs by 90%, keeping costs down to $2.5M annually versus $23.8 million for observability beginners (Enterprise Strategy Group).

For propulsion engineers, these statistics translate into faster root cause analysis, reduced test failures, and more efficient iteration cycles—critical factors in a field where every second of test time is costly.

Rather than relying on rigid monitoring systems, modern propulsion teams need observability platforms that adapt to evolving test scenarios and provide deep insight into system behavior.

Observability in the Real World: From Rocket Engines to Hypersonic Flight

To illustrate the impact of observability, consider its role in modern propulsion system development.

Rocket Engine Testing

Developing and testing a liquid rocket engine involves managing high-speed, high-volume telemetry data streams from hundreds of sensors, including:

• Thrust, mixture ratio, and chamber pressure sensors to monitor combustion efficiency.
• Accelerometers and strain gauges to assess structural loads during firing.
• High-speed pressure transducers to detect combustion instability events.
• Valve timing and actuator performance data to verify sequencing accuracy.

An observability platform allows test teams to rapidly correlate these parameters, identify early indicators of anomalies, and refine engine performance without requiring hours of manual data review.

Hypersonic Vehicle Propulsion

In hypersonic flight, air-breathing engines such as scramjets operate in extreme thermal and aerodynamic conditions. Unlike conventional engines, hypersonic propulsion systems must manage:

• Rapid pressure fluctuations within inlet compression stages.
• Real-time fuel injection adjustments to maintain combustion stability.
• Thermal load balancing across actively cooled engine structures.
• Integrated vehicle-aerodynamic interactions that influence overall performance.

Observability platforms enable engineers to process high-frequency telemetry in real-time, ensuring stable engine operation across Mach 5+ flight conditions.

The Pitfalls of Building Observability In-House

When companies attempt to develop their own bespoke observability solutions, they typically run headlong into a recurring set of challenges:

• Data Centralization – High-speed telemetry requires advanced ingestion pipelines to handle simultaneous sensor streams with sub-millisecond resolution.
• Usability for All Stakeholders – Test engineers, propulsion analysts, and mission controllers all require different levels of access to the same telemetry dataset.
• Real-Time Decision-Making – Test operators need anomaly detection and root cause analysis tools that surface actionable insights instantly.
• Maintainability – In-house solutions are often brittle, requiring ongoing development resources to adapt to evolving test configurations.

The Limitations of IT Observability Tools

Generic IT observability tools fail to meet the needs of propulsion engineers. Traditional database architectures struggle with:

• High-frequency data ingestion – Combustion data often requires sampling rates above 10 kHz, far exceeding standard monitoring tools.
• Scalable query performance – Analyzing multi-terabyte datasets in real-time requires an optimized telemetry infrastructure.‍
• Advanced visualization – Test teams require interactive tools that support complex physics-based analysis, not just basic time-series graphs.

Sift is pioneering the next generation of machine development with the first unified observability stack purpose-built for hardware data.

Sift: Purpose-Built Observability for Propulsion Engineers

Sift is pioneering the next generation of machine development with the first unified observability stack purpose-built for hardware data. Founded by former SpaceX engineers with deep experience building reusable rockets, Sift is tailored for the unique challenges of complex, sensor-rich systems.

Sift's platform is the only comprehensive solution that mitigates risk, automates data review, and provides complete visibility into operational hardware. Sift goes beyond the limits of traditional monitoring tools, putting previously inaccessible insights and capabilities into the hands of engineering teams.

By leveraging automatic data review, contextual alerting, low-latency ingestion, and the ability to test as you fly, Sift empowers engineers with complete situational awareness across their entire vehicle fleet. When the inevitable anomalies arise, Sift's powerful tooling helps teams quickly identify and resolve issues before they lead to costly mission failures.

Sift and the Future of AI-Powered Observability

In the rapidly evolving world of propulsion system development, AI-native observability is redefining how engineers test, refine, and validate complex machinery. From detecting subtle combustion instabilities to optimizing turbomachinery performance, AI is not just an add-on—it’s embedded into the entire data lifecycle. However, for AI to drive real operational impact, it must be built on structured, contextualized, and instantly retrievable data.

Sift ensures that propulsion engineers get AI that actually works by centralizing, normalizing, and time-aligning high-frequency telemetry at scale. Unlike rigid data lakes or fragmented in-house tools, Sift is designed for AI-first observability, leveraging real-time structured data ingestion and the efficiency of Apache Parquet storage. This allows engineers to apply AI-driven anomaly detection, root cause analysis, and predictive failure modeling without the burden of manual data preparation.

To fully realize the potential of AI in propulsion observability, teams need an infrastructure that supports live data orchestration across high-bandwidth sensors and distributed test environments. Sift provides this by decoupling compute and storage, ensuring real-time AI processing without query lag or costly re-indexing. AI-powered anomaly detection and rule generation evolve with each test run, allowing teams to move beyond static thresholds to adaptive, intelligent alerting.

At the core of Sift’s AI-native observability is transparency and engineer-driven control. Instead of black-box machine learning models that obscure insights, Sift delivers deterministic, human-readable AI outputs that integrate seamlessly into existing workflows. Engineers can query data using natural language, generate contextual visualizations, and refine AI-driven insights into operationally relevant, deterministic rules.

With Sift, propulsion teams don’t just get AI as a feature—they get AI as a strategic enabler for real-time decision-making, automated test validation, and continuous performance optimization. As propulsion systems grow in complexity, Sift ensures engineers stay ahead, transforming raw telemetry into a living, evolving intelligence engine that drives better designs, safer tests, and more resilient flight systems.

Observability as Ground Truth

For propulsion engineers, observability is not just an advantage—it is an operational necessity. With engine performance dependent on millisecond-level timing, split-second combustion stability adjustments, and precise fluid dynamics, teams require a single, unambiguous source of truth to maintain control over their systems. Sift’s unified observability platform provides this clarity, enabling engineers to diagnose failures faster and iterate on designs with real-time data.

Rather than spending hours reconciling fragmented logs and raw telemetry, Sift gives propulsion engineers instant access to structured, high-fidelity data. Whether debugging a liquid rocket engine startup sequence or identifying transient pressure spikes in a scramjet, Sift centralizes insights, reducing the guesswork and improving responsiveness.

Sift’s workflow is designed to ensure that institutional knowledge is preserved alongside machine data, enabling seamless collaboration between test engineers, propulsion analysts, and control system designers. By integrating telemetry review from early R&D to full-scale flight testing, Sift helps teams proactively mitigate risks before they escalate into costly mission failures.

• Streamlined Data Management and Accessibility: Understanding propulsion system behavior requires unfettered access to test data. From injector flow rates to high-frequency vibration analysis, engineers must analyze thousands of parameters without being bogged down by inefficient tooling. Sift’s observability platform eliminates data silos, enabling high-speed querying and in-depth analytics that fuel innovation.
• Risk Mitigation: Propulsion failures are costly, often catastrophic. Relying on manual review or preconfigured alert scenarios leaves teams vulnerable to unforeseen issues. Sift’s AI-driven alerting surfaces the most critical anomalies automatically, enabling engineers to react before problems escalate. With automatic data review and contextual alerting, propulsion teams gain confidence in their decision-making, improving safety and reliability.
• Comprehensive Fault Tolerance: Precision propulsion systems—whether for orbital-class rockets or hypersonic vehicles—demand rigorous validation. Sift streamlines fault tolerance testing by automating test data review, ensuring that even minor anomalies are flagged, analyzed, and integrated into future iterations. With Sift, teams can rapidly validate system integrity under extreme operating conditions, reducing development timelines while maintaining quality.
• Intelligent Data Storage and Retention: Propulsion test programs generate immense volumes of telemetry data, requiring efficient storage and retrieval mechanisms. Off-the-shelf cloud storage solutions struggle to balance cost, speed, and long-term data retention. Sift’s scalable architecture is purpose-built to ingest and analyze propulsion telemetry, ensuring that critical data remains accessible while keeping storage costs manageable. Engineers can leverage historical data for comparison, anomaly detection, and long-term trend analysis—without unnecessary overhead.

Simplicity Through Observability

Sift’s observability platform is a force multiplier for propulsion teams. By delivering an end-to-end solution designed specifically for high-performance, sensor-rich systems, Sift eliminates the inefficiencies of fragmented data pipelines. Engineers no longer need to piece together partial insights from multiple tools—instead, they gain a single, intuitive platform that accelerates troubleshooting, iteration, and system optimization.

Beyond saving time and resources, Sift’s automated workflows capture and retain institutional knowledge, ensuring that insights gained from early-stage development persist through vehicle integration and flight operations. With real-time visibility into every aspect of propulsion performance, teams can focus on pushing the boundaries of what’s possible, rather than wrangling disjointed datasets.

As propulsion technology evolves, the demand for high-fidelity observability will only increase. Sift provides the tools to future-proof test operations, ensuring that teams can adapt to new challenges without being held back by outdated data systems. By investing in purpose-built observability, propulsion teams safeguard their mission success—minimizing human error, maintaining institutional knowledge, and accelerating the pace of aerospace innovation.

The future of propulsion belongs to those who can see clearly. With Sift, engineers have the observability they need to power the next generation of high-performance flight systems.

To learn how Sift can enhance observability for your mission-critical defense systems, schedule a demo with our team today.