Building Real-Time Telemetry Pipelines for IRIG 106 compliance

By Allyson Boate
May 15, 2026 Developer

The need for real-time telemetry in aerospace

Every second of a flight test produces a torrent of telemetry from engines, sensors, and control systems. Aerospace teams have captured this data for decades to verify performance and maintain safety, yet analysis often happens long after the mission ends. Engineers wait for downloads, conversions, and compliance checks before they can interpret results.

That delay turns telemetry into a historical record instead of a feedback loop. As flight programs shorten development cycles and expand digital testing, teams need to see and act on telemetry as it arrives. Real-time visibility turns raw packets into insight and enables faster, more confident decisions mid-test.

What is IRIG 106?

IRIG 106 forms the backbone of flight-test telemetry. Established by the Range Commanders Council, it defines how data is formatted, synchronized, and recorded to ensure interoperability across recorders, ground stations, and analysis tools. Its purpose is to create a shared language for flight-test instrumentation so every team, from acquisition to post-flight analysis, can exchange and interpret telemetry without loss or confusion.

By standardizing time, metadata, and sensor data, IRIG 106 ensures that complex flight tests remain reproducible and comparable across aircraft and programs. It allows flight data from one system or site to be understood by another, a foundation for multi-agency and multi-system collaboration.

Chapter 10 is the most widely used section. It defines a packetized structure for analog and digital sensors, time codes, video, and bus data, each with embedded metadata describing its stream. This structure preserves timing, organization, and integrity across the workflow.

For aerospace and defense teams, Chapter 10 compliance is essential for traceability and certification. While it guarantees rigor, the binary packet format slows analysis.

Compliance vs. agility

Traditional telemetry pipelines were built for compliance, not speed. Data flows from airborne recorders to ground systems, where it’s stored in proprietary or binary Chapter 10 files. These files are durable but heavy, often requiring decoding or conversion before engineers can analyze trends.

This gap between collection and insight resuts in terabytes of data sitting idle until post-flight processing is complete. Even simple questions such as “Did this vibration spike correlate with an actuator command?” must wait for hours of decoding.

The cost is real. Missed anomalies can trigger additional tests, wasted fuel, and schedule delays. Commercial operators lose flight hours, while defense programs face slower certification and reduced mission readiness. Each delay compounds across teams, consuming engineering hours and analysis budgets that could be spent improving system performance. As systems grow more software-defined and autonomous, reactive analysis becomes increasingly expensive. Teams must maintain compliance while gaining agility, turning telemetry into a live, searchable data stream that drives faster, data-backed decisions grounded in data integrity.

Building a real-time, compliant telemetry pipeline

For aerospace organizations, InfluxDB 3 bridges the gap between strict IRIG 106 compliance and the agility needed for real-time telemetry analysis. Built on an open columnar foundation, it treats every measurement as part of a continuous record of system behavior optimized for rapid ingest and millisecond-level queries.

InfluxDB 3 combines streaming ingestion, high-compression storage, and integrated compute into a single environment. Instead of exporting data between collection, transformation, and analysis systems, engineers work with telemetry where it lands. They can transform data on ingest, query it with SQL, or run analytics through the built-in Python Processing Engine, all in one place.

The result is an architecture that maintains compliance and precision while delivering the responsiveness and scalability aerospace programs demand. With the right connectors, Chapter 10-compliant recorders can stream decoded data directly into InfluxDB, where it becomes available for dashboards, analytics tools, and ML pipelines while retaining a compliant source-of-truth record in the background.

From ingest to insight

A compliant real-time telemetry pipeline follows five key stages that preserve Chapter 10 structure while enabling high-performance analytics.

1. Acquisition

Airborne systems record simultaneous data, includinganalog, digital, video, and bus data, in Chapter 10 format. Each source is encapsulated in packetized blocks with synchronized time codes and metadata headers. Ground stations receive this data over UDP or Ethernet, maintaining deterministic playback.

2. Decoding

A decoding service reads the binary stream, extracts headers, and separates channels into structured records with timestamps and metadata such as subsystem or bus ID. This step can use open source telemetry libraries or adapters that translate packets into structured formats like JSON or Apache Arrow.

3. Streaming Ingestion

Decoded data is sent to InfluxDB 3 using lightweight producers such as Telegraf, Kafka, or InfluxDB Line Protocol. Each channel becomes a discrete series tagged by aircraft ID, subsystem, and signal type. The ingestion engine supports millions of writes per second, compressing data in memory before persisting it to Parquet files with nanosecond timestamps for cross-sensor correlation.

4. Processing and Downsampling

InfluxDB 3’s embedded Python Processing Engine allows transformations near the data. Engineers can smooth signals, compute FFTs, or derive metrics without external compute clusters. Downsampling in InfluxDB 3 automates data reduction—for example, converting 1 kHz vibration data into 10 Hz averages for long-term storage—while keeping full resolution for recent test windows.

5. Query and Visualization

Once stored, telemetry is immediately queryable through SQL or APIs. Engineers visualize live data, join channels, and correlate responses in real-time. Because InfluxDB 3 uses Parquet and Arrow, it integrates with external analytics tools such as Apache Arrow, Pandas, and DuckDB. Dashboards update continuously as new packets arrive, tracking vibration, control surfaces, or engine parameters throughout the mission.

Together, these stages turn Chapter 10-compliant telemetry into a continuously updating dataset that maintains synchronization and metadata integrity while providing immediate visibility for validation, anomaly detection, and optimization.

Typical Deployment

The flow looks like this:

Recorder → Decoder → Stream Processor → InfluxDB 3 → Visualization or ML Pipeline

• The recorder collects Chapter 10-compliant telemetry, aligning all channels with precise time codes.
• The decoder extracts packet data and converts it into structured messages for ingestion.
• A stream processor such as Telegraf or Kafka Connect forwards those messages to InfluxDB 3, where they are indexed and persisted as time-aligned measurements.
• Engineers access data through dashboards, notebooks, or Python APIs, enabling live visualization and downstream machine learning or simulation workflows.

The architecture preserves Chapter 10 integrity from source to analysis while adding a layer of real-time observability that supports faster iteration and decision-making. For multi-site telemetry systems, teams can extend this approach using distributed historian architectures with InfluxDB 3.

Real-time telemetry in action

Consider a typical aerospace testing scenario. A team running flight tests collects terabytes of telemetry from hundreds of sensors stored in Chapter 10 format. Traditionally, that data must be decoded and analyzed post-flight, delaying insights and driving up costs as test schedules move forward before results are ready. In a real-time telemetry pipeline built on InfluxDB 3, that same data becomes available the moment it’s collected. Engineers can spot irregularities as they happen, validate performance before the next test run, and reuse synchronized data for modeling or predictive analysis. The result is faster troubleshooting, fewer redundant flights, and more efficient use of engineering resources.

Faster flight-test analysis and decision-making

Real-time telemetry pipelines mark the next phase of aerospace testing. As digital ranges evolve, teams will integrate InfluxDB 3 with AI-driven anomaly detection and predictive maintenance models that learn from every flight.

By modernizing how IRIG 106 data is collected, stored, and analyzed, aerospace organizations can shift from compliance-driven testing to intelligence-driven improvement. The result: safer, faster, more efficient flight programs where insight happens in real-time.

Ready to explore how these architectures work in practice? Get started with InfluxDB 3 for free or watch our webinar to see how aerospace teams use Influxdb 3 for real-time data.