InfluxData Blog - Allyson Boate

Satellite Telemetry, ITAR, and Data Residency: Building Architecture for Speed and Control

Allyson Boate (InfluxData) — Thu, 11 Jun 2026 08:00:00 +0000

Satellite mission operators depend on telemetry to understand spacecraft health, ground system performance, and mission status in real-time. Operation signals help teams identify risks, investigate anomalies, and keep operations moving.

When a spacecraft enters safe mode or signal strength drops during a contact window, teams need trusted telemetry immediately. But mission data moves quickly across operational systems, and every handoff makes it harder to control.

How can teams keep telemetry fast, useful, and available while maintaining control over sensitive mission data?

Why ITAR and data residency matter for telemetry

For satellite operators, sensitive mission data raises two practical questions: who can access the data, and where can it go? ITAR and data residency requirements bring those questions into the monitoring conversation.

ITAR, or International Traffic in Arms Regulations, controls how certain defense-related products, services, and technical data can be shared. In practice, these rules help prevent sensitive information from moving to unauthorized people, systems, or environments.

Technical data does not always stay attached to hardware, but can show how spacecraft systems operate, perform, fail, or respond under real conditions. Battery temperature, solar panel output, signal strength, contact window performance, and subsystem fault codes help teams monitor spacecraft health; those readings may also reveal performance limits or operational patterns that require closer review.

Legal and compliance teams determine which telemetry falls under ITAR or other export-control requirements. Classification alone does not protect the data. Engineering and infrastructure teams need systems that enforce those decisions as telemetry moves through daily operations.

Data Movement Creates the Control Challenge

Data residency adds the next layer. Telemetry may originate from spacecraft, ground systems, payload systems, and mission infrastructure, then move through ground stations, dashboards, cloud tools, analytics workflows, vendor systems, and long-term archives. Each stop creates another place where mission data may live, get copied, or become accessible.

When teams cannot trace readings across those systems, compliance reviews take longer and ownership becomes harder to prove. Security teams may struggle to confirm who accessed the data, where copies exist, and which system serves as the source of truth. Mission teams may also lose time reconstructing event timelines across systems.

The architecture needs to answer the practical questions behind the compliance review: where mission data lives, who can access it, how long teams retain it, and how it moves across systems.

What data sprawl costs mission teams

Data silos and sprawl create the most risk when mission teams need to act quickly. During an anomaly, engineers need a clear sequence of events: what changed, when it changed, and which systems contributed.

A ground station contact window can expose the cost. A satellite reports rising battery temperature, irregular power draw, and a sudden safe mode event. To identify the cause, engineers need to trace battery temperature, power draw, command history, subsystem fault codes, and communications data from the minutes leading up to the event.

When each data source lives in a different tool, the response slows. One team checks a dashboard, another pulls logs from cloud storage, and another reviews an exported file from an analytics workflow. Engineers spend critical time reconciling records instead of isolating the issue. Those disconnected workflows create operational and governance costs at the same time. Mission teams lose speed during anomaly response. Compliance and security teams lose visibility into where sensitive telemetry lives, who can access it, and which copies exist.

How InfluxDB 3 supports controlled telemetry monitoring

With InfluxDB 3, satellite teams can bring high-volume operational signals from spacecraft, ground systems, and infrastructure into a shared time series architecture. When mission information lives across disconnected dashboards, logs, and exports, engineers have to piece together telemetry from multiple systems. With a shared time series architecture, engineers can analyze time-organized signals in one place, compare readings against historical baselines, and respond faster when anomalies occur.

How the Architecture Works

A controlled data architecture starts with ingestion. Telemetry from spacecraft, ground systems, payload systems, and mission infrastructure can flow through Telegraf agents, MQTT pipelines, or other approved collection paths into InfluxDB 3 as time-stamped data. Tags add operational context, such as spacecraft ID, subsystem, ground station, or signal source.

Once telemetry enters the database, teams can query across systems without moving data into separate files or one-off tools. Engineers can compare current signal strength against previous contact windows, review power draw before a safe mode event, and correlate reaction wheel performance with temperature changes over time.

Dashboards and alerts can use the same telemetry record. Operators can monitor live spacecraft health, trigger alerts when values drift from expected ranges, and investigate anomalies with historical context. Retention and downsampling extend the workflow over time, helping teams keep high-resolution telemetry where detail matters and preserve long-term trends as data ages.

Deployment Flexibility for Sensitive Data

For satellite operators working with sensitive data, flexibility matters. Without deployment control, organizations risk signals moving outside approved environments, granting access to the wrong users or systems, and creating copies that complicate internal reviews.

InfluxDB 3 Core gives teams a self-managed option for real-time telemetry ingest and recent-data queries in edge, on-premises, or private cloud environments. Self-managed deployment helps teams keep time series workloads closer to mission operations, ground systems, or other reviewed infrastructure.

InfluxDB 3 Enterprise builds on that foundation for production workloads. High availability helps maintain access to mission data during critical operations. Read replicas can support dashboards, investigations, and analytics traffic without putting extra pressure on ingest workloads. Multi-node deployment options help teams separate ingest, query, and compaction as data volumes grow. While InfluxDB 3 does not determine whether telemetry falls under ITAR or make an organization compliant by default, it aligns telemetry workflows with internal requirements for storage, access, retention, and deployment.

Eutelsat OneWeb: Satellite Telemetry at Scale

Eutelsat OneWeb puts this deployment flexibility into play. The company operates a hybrid constellation with more than 600 LEO satellites, each producing more than 50,000 values. Across the constellation, the operations team processes more than 1 million data points per second.

At this scale, they needed a platform that could handle high-volume time series data, support real-time monitoring, and help engineers analyze spacecraft and ground-segment behavior in one place.

The company built a telemetry stack with InfluxDB as the centralized time series engine, Telegraf agents across the ground segment, and Grafana for dashboards, alerting, and cross-system correlation. InfluxDB supports more than 15 million unique series.

This architecture gives the team a unified way to explore spacecraft and ground-segment data side by side. Engineers can monitor satellite health, correlate time series data across systems, trigger alerts from InfluxDB queries, and replay events for root-cause analysis. With that shared operational timeline, the team can analyze mission behavior across spacecraft and ground-segment systems.

Read the full case study to check out how Eutelsat OneWeb uses InfluxDB to manage satellite telemetry.

The bottom line

Satellite telemetry needs to move fast, but sensitive mission data also needs control. When telemetry spreads across disconnected systems, teams lose time during anomaly response and confidence during compliance review. A unified time series architecture helps satellite operators keep telemetry queryable, comparable, and governed across live operations and historical analysis.

To get started, explore InfluxDB 3 Core OSS or InfluxDB 3 Enterprise to see how time series architecture can support real-time mission visibility, historical analysis, and controlled data workflows.

Building Real-Time Telemetry Pipelines for IRIG 106 compliance

Allyson Boate (InfluxData) — Fri, 15 May 2026 12:00:00 +0000

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.

Unifying Telemetry in Battery Energy Storage Systems

Allyson Boate (InfluxData) — Thu, 19 Mar 2026 08:00:00 +0000

Battery energy storage systems (BESS) play a critical role in modern energy infrastructure. Utilities rely on these systems to balance renewable generation, stabilize grid operations, and respond to changing electricity demand. As deployments scale in size and complexity, operators require continuous insight into battery health, system performance, and grid interaction. Operators rely on telemetry generated across several operational platforms. Battery management systems monitor cell behavior, power conversion systems, and regulate energy flow, while plant control platforms track facility status. Energy management software and environmental sensors provide additional context about facility conditions.

In many deployments, however, this information remains scattered across separate monitoring environments. Operators often move between multiple dashboards to understand activity across a single facility. Many BESS operators are now adopting unified telemetry platforms that consolidate operational signals and create a clearer operational view of system behavior.

The operational reality of modern BESS systems

A battery energy storage facility is not a single system but a collection of specialized subsystems that manage energy storage, power conversion, and grid interaction. Each subsystem monitors a different aspect of facility performance and generates operational signals that help operators understand how the system behaves.

Several platforms produce these signals. Battery Management Systems (BMS) track cell-level conditions such as voltage, temperature, and state of charge to protect battery health. Power Conversion Systems (PCS), typically implemented through inverters, regulate how electricity flows between the battery and the grid.

Plant-level monitoring runs through SCADA platforms, which provide alarms, system status, and operational controls. Energy Management Systems (EMS) determine when energy should be stored or dispatched based on grid signals and market conditions, while environmental sensors monitor external factors such as ambient temperature.

Together, these systems create a continuous operational record of facility performance, but the resulting information does not always exist in a shared environment.

The fragmented reality of BESS telemetry

In most battery energy storage deployments, operational data originates from multiple independent platforms, as described above. This fragmentation reflects the modular design and deployment of energy storage facilities. Battery systems, power conversion equipment, and plant control platforms are frequently delivered by different vendors, each with its own software, data models, and monitoring tools.

Because these platforms monitor individual components rather than the entire facility, data is rarely consolidated automatically. Operators often rely on multiple dashboards to understand activity across a single storage site. Correlating events between subsystems may require switching between tools and manually comparing timestamps or operational signals.

The result? Operators have access to large volumes of operational information but lack a unified view of the facility as a whole. When events occur across multiple subsystems, understanding how those signals relate to one another requires time and effort.

Operational cost of data silos

Even small issues can require significant labor to diagnose. The data silos created by ala carte technologies prevent engineers from seeing how signals across the storage system relate.For example, a thermal anomaly—an unexpected rise in battery temperature—may require operators to review battery readings, compare inverter load behavior, and examine environmental conditions. Without a unified view of these signals, determining the cause can take time.

These delays affect both system reliability and financial performance. If operators cannot quickly determine why system capacity dropped or alarms triggered, dispatch readiness may be affected during critical market windows. Over time, slower investigations and delayed anomaly detection can lead to reduced system availability, higher operational overhead, and missed revenue opportunities.

What unified telemetry actually means

Unified telemetry consolidates operational signals from across the storage system into a shared data environment. Instead of storing data separately within subsystem platforms, telemetry from across the facility enters a common dataset.

In this environment, operational signals are stored as time-series data, or measurements organized by timestamp, allowing signals from different subsystems to be synchronized and analyzed together.

This shared dataset allows engineers to correlate signals that were previously isolated. Battery temperature trends can be examined alongside inverter load behavior, dispatch signals, and environmental conditions to better understand system performance. Instead of switching between monitoring platforms, operators can observe how signals across subsystems evolve together within a unified operational timeline.

How unified telemetry works

In many deployments, telemetry aggregation begins at the edge of the facility. Edge collectors connect to operational systems such as the BMS, PCS, SCADA platform, EMS and environmental sensors using industrial protocols such as Modbus, OPC-UA, or CANbus. These collectors ingest operational signals and convert them into structured telemetry streams.

From there, the data flows through streaming pipelines into centralized platforms. These pipelines handle ingestion, buffering, and transport of high-frequency signals so information from across the facility can be processed as a continuous operational stream.

Time series databases store and index this telemetry by timestamp, allowing engineers to query system behavior over time. Organizing operational signals this way enables teams to correlate events across subsystems, analyze performance trends, and investigate anomalies.

Because signals from different systems exist in the same time-aligned dataset, engineers can examine battery performance, inverter activity, dispatch signals, and environmental conditions together. This enables faster incident investigation and supports advanced analysis such as anomaly detection and predictive maintenance.

Operational impact

Unified telemetry changes how energy storage facilities are operated and how organizations manage risk, reliability, and revenue. When signals from battery systems, power electronics, and plant controls are analyzed together, operators gain a comprehensive view of facility behavior rather than having to reconstruct events across multiple monitoring platforms.

This visibility allows teams to detect anomalies earlier and respond to operational issues before they escalate. Faster diagnosis reduces downtime and helps maintain system availability during critical dispatch windows. In energy markets, maintaining dispatch readiness helps protect revenue during high-value trading periods.

ju:niz Energy Deployment

ju:niz Energy operates large-scale battery storage systems that provide grid services and trading flexibility in energy markets. Their systems collect thousands of data points per second on battery health, temperature, climate conditions, and system performance.

To manage this telemetry, ju:niz built a centralized monitoring architecture using Telegraf, Modbus, MQTT, Grafana, Docker, AWS, and InfluxDB. Operational signals from battery systems stream into a centralized time series platform, giving engineers a unified view of system behavior and eliminating the need for legacy Python monitoring scripts.

This architecture enables the ju:niz team to analyze battery telemetry in real-time, improve alerting accuracy, and support predictive maintenance strategies across their storage infrastructure.To see how ju:niz implemented unified telemetry for its operations, read the full case study or watch the webinar.

The bottom line

Battery energy storage systems generate telemetry across multiple operational platforms, but when that data remains fragmented, operators struggle to understand how the system behaves as a whole. Unified telemetry solves this by bringing operational signals into a shared, time-aligned dataset. As BESS deployments scale, this capability will become foundational for operating energy storage systems reliably, efficiently, and profitably.

Ready to build a unified telemetry architecture? Get started with a free download of InfluxDB 3 Core OSS or a trial of InfluxDB 3 Enterprise.

From Reactive to Predictive: Preserving BESS Uptime at Scale

Allyson Boate (InfluxData) — Thu, 05 Mar 2026 08:00:00 +0000

Battery Energy Storage Systems (BESS) operate as revenue-generating grid assets that capture surplus electricity, deploy power during demand spikes, and support frequency control. By shifting energy across time, they stabilize grid conditions, enable renewable integration, and execute market dispatch commitments. When systems respond as designed, stored capacity becomes a flexible, monetizable supply.

But BESS performance depends on precision and availability. When deviations in temperature, voltage, or current go undetected, instability can propagate across battery modules and supporting systems. Dispatch commitments fail, contractual penalties follow, and safety exposure increases.

In large-scale deployments, uptime becomes a financial and operational control variable rather than a maintenance metric. Preserving availability requires more than reacting to alarms after limits are breached. As fleets expand and system complexity grows, reactive monitoring reaches its ceiling.

What is a BESS?

A Battery Energy Storage System (BESS) is a grid-connected battery infrastructure that stores electricity when supply exceeds demand and deploys it when demand rises. By shifting energy across time, these systems help balance generation and consumption while supporting market commitments and frequency control. Their value lies not only in storing energy, but in responding precisely when grid conditions change.

Electrical supply and demand must remain balanced at all times. When surplus power enters the grid, a BESS absorbs that energy and holds it until demand increases, at which point stored electricity is released back into the network. This coordinated charge-and-discharge cycle enables controlled energy movement that stabilizes supply, supports renewable energy sources, and maintains consistent grid performance.

Storage systems adjust output within seconds to correct short-term imbalances. Rapid response smooths fluctuations from wind and solar generation and helps maintain grid stability. As more renewable energy comes online and demand patterns shift, reliance on storage systems increases. In this environment, availability and response speed directly influence reliability and financial performance.

Availability as an Operational Variable

The value of a BESS depends on its availability. When a system goes offline, dispatch capacity contracts immediately, and stored energy cannot be delivered as planned. Market commitments may go unmet, and replacement capacity must be sourced elsewhere, resulting in lost revenue, potential penalties, and increased operational expenses.

In large-scale deployments, availability becomes more complex to manage. Thousands of battery modules operate simultaneously, each producing continuous temperature, voltage, and current data. These modules function as a coordinated system, in whichwhere small issues in one area can affectinfluence overall performance. As fleet size grows, operational oversight becomes more demanding.

Uptime is more than a maintenance metric. It directly affects revenue performance, capacity payments, and grid commitments. Even small disruptions can reduce dispatch capability before a full outage occurs. Preserving availability requires visibility that scales with system complexity.

The limits of reactive monitoring

Operational failures in BESS environments rarely begin as sudden outages. They often start as gradual shifts in temperature, voltage, or current that move systems toward instability while remaining within acceptable limits. These early changes can appear normal when viewed in isolation.

Most monitoring systems rely on predefined thresholds to detect abnormal conditions. An alert is triggered only after a value crosses a set boundary, confirming that a limit has already been breached. By the time an alarm activates, the underlying condition may have been developing for hours or days. The opportunity for intervention narrows.

Telemetry is often distributed across battery management systems, inverter controls, and environmental monitoring platforms, creating data silos across operational layers. Each system captures a portion of operational behavior, but signals are reviewed separately and correlated manually. This separation makes it difficult to see how conditions evolve across modules. Engineers spend valuable time assembling context rather than acting on it.

As deviations compound, risk increases. Capacity can drop offline, dispatch commitments may fail, and safety exposure rises. Reactive monitoring preserves awareness of failure, but does not preserve control.

Thermal Runway

Thermal runaway is one example of how small battery deviations can escalate when not addressed early. A gradual rise in temperature can accelerate internal reactions and generate additional heat. Without timely correction, this cycle can intensify and spread to neighboring cells. What begins as minor drift can trigger protective shutdown mechanisms designed to prevent damage. While necessary for safety, shutdown interrupts dispatch commitments and reduces available capacity. Lost availability affects revenue performance and may introduce regulatory and safety exposure. The longer that instability goes undetected, the greater the operational impact.

Predictive monitoring extends control

Predictive monitoring evaluates how operational signals change over time rather than reacting only after limits are breached. Temperature, voltage, and current readings are analyzed as evolving trends across battery modules, allowing engineers to see how conditions develop instead of viewing each signal in isolation. The value lies not only in collecting data, but in understanding how system behavior shifts as signals change together.

In large BESS deployments, thousands of modules generate high-frequency telemetry that reflects thermal and electrical conditions. When these signals are reviewed independently or only against static thresholds, gradual drift can appear routine. Evaluated within a shared time context, emerging patterns become visible across modules and clarify where intervention is required.

Time series data reflects current operating conditions, while historical data preserves baseline behavior and long-term performance trends. Comparing live readings against historical baselines distinguishes normal variation from early signs of degradation. By combining immediate visibility with long-term context, operators can intervene before instability propagates.

Real-time Analysis with InfluxDB

InfluxDB is purpose-built for time-series workloads that require high ingestion rates, scalable retention, and fast analytical queries. It captures continuous telemetry from distributed battery systems and organizes it using time-based indexing and columnar storage structures optimized for time-stamped data. Its value lies not only in storing operational signals, but in preserving query efficiency as data volume increases.

As BESS fleets expand, ingestion and query demand rise simultaneously. Temperature, voltage, and current streams must be written at scale while remaining immediately available for investigation. InfluxDB applies compression and retention policies that balance long-term historical context with storage growth. This design maintains visibility at scale without slowing down dashboards or investigative workflows.

Real-time analysis and historical comparison occur within the same execution path. Engineers can evaluate gradual drift and investigate emerging instability without exporting data to separate systems. Downsampling strategies preserve long-term trend visibility while keeping high-resolution data available for recent events. This unified architecture reduces operational overhead and preserves intervention windows under load.

Predictive monitoring in action

Siemens Energy uses InfluxDB to standardize predictive maintenance across distributed energy and battery storage operations. High-frequency sensor telemetry from production systems and battery deployments is ingested into a unified time-series platform that preserves both real-time visibility and long-term historical context. Its value lies not only in collecting large volumes of operational data, but in maintaining consistent access as systems expand across sites and regions.

Across more than 70 global locations and approximately 23,000 battery modules, continuous temperature, voltage, and performance signals are captured and stored within the same environment. Time-based indexing and scalable retention policies ensure that high-resolution data remains accessible for immediate analysis while preserving long-term degradation trends. This coordinated data architecture enables engineers to evaluate system behavior across modules rather than reviewing signals in isolation.

The verdict

BESS assets operate within narrow operational and financial tolerances where availability directly influences revenue, safety, and grid reliability. Reactive monitoring confirms when limits are crossed, but predictive monitoring preserves visibility into how conditions evolve before capacity is affected. As fleets expand and telemetry volume increases, infrastructure must ingest high-frequency signals, retain historical context, and return results without latency. When time-series architecture aligns with the structure of operational data, predictive maintenance scales with system complexity rather than breaking under it, preserving uptime across large BESS environments.

Ready to move from reactive monitoring to predictive control? Get started with a free download of InfluxDB 3 Core OSS or a trial of InfluxDB 3 Enterprise.

From Monitoring Signals to Observability Maturity

Allyson Boate (InfluxData) — Thu, 22 Jan 2026 08:00:00 +0000

Efficient monitoring delivers fast results: alerts fire within seconds, dashboards refresh continuously, and teams know the moment something changes.

Understanding arrives later. An alert may show that a value shifted, but it does not explain why it shifted, how far the impact will spread, or which components truly matter. Teams see the signal, not the system behavior behind it.

This gap defines the limit of traditional monitoring. Detection has improved, but explanation has not kept pace. As environments grow more interconnected, reporting change without context leaves teams reacting instead of understanding. Mature monitoring must explain behavior and impact, not just surface signals.

When context falls apart

Without context, change is difficult to interpret. An alert may confirm that something happened, but it rarely explains which dependencies influenced the behavior, how far the impact might spread, or which components are truly affected. Dashboards emphasize individual services or resources, while alerts trigger based on local thresholds instead of system-wide impact. Teams see the signal, but not the behavior behind it.

In most environments, the missing context lives elsewhere. Dependency information is scattered across configuration files, infrastructure tools, and service catalogs. Ownership and escalation paths live in runbooks. Historical relationships are reconstructed during incidents through manual analysis or ad hoc queries. This separation creates data silos, forcing teams to stitch together metrics, metadata, and system structure to gain a comprehensive view of what is actually happening.

The Cost of Fragmented Visibility

As environments scale, fragmentation becomes expensive. Root cause analysis slows as engineers trace upstream and downstream impact across multiple tools. Alert fatigue increases when signals cannot be evaluated against real dependencies. Mean time to resolution grows as teams spend more effort assembling context than resolving the issue. Even when an anomaly is detected quickly, understanding why it occurred and what it affects often arrives too late to prevent broader disruption.

The impact extends beyond incident response. Capacity planning becomes less reliable when demand shifts propagate through systems that teams cannot easily trace. SLO and SLA tracking lose precision when alerts lack impact awareness. Automation remains cautious or brittle because signals do not consistently reflect the true system state. What begins as a context gap turns into operational overhead, engineering toil, and inconsistent customer experience. Closing the gap between detection and understanding requires monitoring to evolve beyond reporting change and toward explaining system behavior and impact.

From signals to system understanding

When monitoring evolves beyond reporting signals, it gives teams the context needed to understand system behavior and impact. Observability maturity shifts monitoring from answering when something changed to explaining why it changed and what it affects. Signals no longer arrive as isolated data points; they are interpreted within the system that produced them.

With this context, teams can assess impact as soon as a signal appears. A latency spike is not just a breached threshold. It shows how activity in one component influences others, which dependencies are involved, and whether the change represents localized noise or broader risk. This perspective supports faster, more proportional responses and reduces unnecessary remediation.

When Signals Gain Meaning

As monitoring practices mature, investigations become more focused and efficient. Teams spend less time assembling dashboards or reconciling data across tools. Signals are evaluated alongside related components, making root cause analysis more transparent and reducing the effort required to identify contributing factors. Mean time to resolution (MTTR) improves because understanding arrives earlier in the response cycle.

Observability maturity also strengthens day-to-day operations. Historical telemetry reveals patterns that inform capacity planning, SLO and SLA management, and reliability goals. Alerting becomes more effective when signals are evaluated in context rather than isolation, helping reduce alert fatigue. Automation becomes safer to trust because actions reflect a clearer view of system state and impact.

In this model, monitoring supports confident decision-making. Teams move away from reactive firefighting and toward proactive operations, using telemetry not only to detect change, but to understand how systems behave as environments grow more interconnected.

How observability maturity becomes possible

Observability maturity depends on a platform that can ingest, store, and analyze telemetry within a unified execution environment. Metrics, events, and time series data must flow through the same data paths so teams can correlate change across systems rather than reconstructing context through downstream tooling or manual analysis.

By unifying ingestion and querying for metrics, events, and telemetry, InfluxDB 3 provides a time series–first observability platform that supports infrastructure, applications, edge deployments, and industrial systems through a single data model.

A Unified Telemetry Foundation

Modern environments generate large volumes of time-stamped data with rapidly changing dimensions. Supporting observability maturity requires handling high-cardinality time series data without degrading ingest performance or query latency.

InfluxDB 3 is built on a columnar analytics stack using Apache Arrow for in-memory execution and Parquet for durable, compressed storage. Telemetry flows through a single ingest path and is stored in a format optimized for analytical access, allowing recent signals and long-term history to be queried through the same interface. This design lets teams analyze live behavior, compare it to historical baselines, and identify trends without maintaining parallel storage systems or export pipelines.

Scale Without Fragmentation

As telemetry volume increases, many organizations separate ingestion, storage, and analysis into different systems. While this can address isolated scaling concerns, it fragments execution paths and makes correlation harder over time. Signals, metadata, and historical context drift into separate layers, increasing query complexity and slowing investigation.

InfluxDB 3 avoids fragmentation by keeping telemetry, metadata, and related observations within a single execution environment. Queries are planned and executed through a unified SQL engine built on DataFusion, allowing joins, filters, and aggregations to run across live and historical data without external synchronization or ETL. This preserves consistency as environments grow and keeps analysis close to the data.

Open Integration and Interoperability

Observability maturity builds on existing tools rather than replacing them overnight. Telemetry must move easily between collectors, visualization layers, automation systems, and analytics workflows. Open interfaces make this possible without forcing teams into proprietary paths.

InfluxDB 3 provides open APIs and a broad integration ecosystem, allowing telemetry to flow freely between systems while maintaining a shared source of truth. Data is stored in scalable object storage using columnar formats, supporting long retention and elastic growth without changing query behavior or operational workflows.

Analysis Close to the Data

As observability practices advance, analysis increasingly runs alongside ingestion and storage. Executing queries where data lives reduces latency and avoids inconsistencies introduced by exporting telemetry to downstream systems.

By executing analytics within the same Arrow-based environment that stores the data, InfluxDB 3 supports correlation, pattern analysis, and advanced workflows without adding architectural layers. Aligning ingestion, storage, and analysis in a single platform provides the technical foundation for monitoring practices to mature into observability at scale.

From monitoring signals to monitoring nirvana

Detecting change is no longer the challenge. Interpreting what that change means across an interconnected system is. As environments grow more complex, observability maturity depends on the ability to connect telemetry with context and history so teams can understand behavior, impact, and progression rather than reacting to isolated signals.

InfluxDB 3 makes this possible by bringing ingestion, storage, and analysis together in a single platform. With telemetry flowing through one execution path, teams maintain consistent context as systems scale. This reduces investigative friction, shortens time to insight, and gives teams the confidence to operate and automate in dynamic environments.

Get started

Try InfluxDB for free: Launch a fully-managed instance and see how modern monitoring works in your environment.

Explore documentation: Access guides, integrations, and examples to help you connect systems and build monitoring pipelines.

Time Series Meets Graph: Understanding Relationships in Streaming Data

Allyson Boate (InfluxData) — Thu, 15 Jan 2026 08:00:00 +0000

Data systems rarely operate as isolated components. Machines depend on sensors, services rely on other services, and devices exchange data through shared gateways. When something changes, the impact often spreads beyond a single metric.

To trace how changes move through complex systems, many teams turn to graph-style analysis to map dependencies and follow cause and effect. While this insight helps teams understand impact and spread, relying on a separate graph database often introduces friction through data duplication, cross-system queries, and added infrastructure. The challenge is not understanding relationships, but connecting them to time-based events without increasing complexity.

Fragmented views of system behavior

Teams collect large volumes of time series data to monitor performance, usage, and reliability. Metrics and events support alerting, SLO tracking, and performance baselining by showing when values change and how quickly conditions shift. What they do not provide is clear visibility into how those changes relate to other parts of the system.

When relationship data, or information about how components connect, is scattered across configuration files, infrastructure tools, application code, or external systems, it creates data silos. Dependencies between services, machines, devices, or pipelines are modeled separately and joined later through manual analysis or downstream tooling. This separation makes it difficult to perform root cause analysis, trace upstream and downstream impact, or correlate anomalies with the components that influenced them. Teams spend more time assembling context than responding to what they see.

As environments scale, this fragmentation creates blind spots and data silos. Cascading events unfold across interconnected components before teams recognize the pattern. An anomaly detected in one service may actually originate in an upstream dependency. Capacity constraints emerge when demand shifts propagate through systems that teams cannot easily trace. By the time teams connect the dots, performance issues often reach customers or downstream systems.

When Dependency Context Lives Elsewhere

Consider a SaaS application built on a microservices architecture. One afternoon, time series metrics show rising latency on a customer-facing API, triggering an alert. During incident response, teams focus on the API itself, scaling instances and reviewing recent changes, yet performance does not recover.

The root cause sits upstream. A shared database experienced contention minutes earlier, increasing latency across multiple service dependencies. Because dependency mapping and service topology live in configuration files and infrastructure tooling, teams cannot easily trace upstream and downstream impact during investigation. By the time the full blast radius becomes clear, customers have already experienced degraded performance.

This delay carries a real cost. Mean time to resolution increases. SLO violations and SLA breaches trigger support escalations. Engineering time shifts from delivery to reactive troubleshooting, increasing operational overhead and engineering toil. Revenue risk grows as customer experience suffers, not because teams lacked data, but because relationship context remained disconnected from events.

Graph insight without a separate graph database

InfluxDB 3 brings timelines and system structure into a single analytical view. Instead of separating events from the relationships that shape them, teams can examine how changes propagate across connected components within a single workflow. This connected perspective moves analysis beyond isolated signals and toward system-level understanding.

Connecting Timelines and Relationships

The shift comes from combining time series data with graph-based thinking. Time series data captures when values change and how quickly conditions shift. Graph-style modeling adds structural context by describing how services, machines, devices, or pipelines relate to one another. Together, these perspectives reveal how activity in one part of the system influences behavior elsewhere.

InfluxDB 3 enables this through relational analysis rather than a dedicated graph database. Relationships are modeled directly in schemas and queried using SQL JOIN. This approach allows teams to follow dependency paths, correlate anomalies with related components, and evaluate upstream and downstream impact using the same engine that stores metrics and events.

Reducing Overhead and Improving Outcomes

Because this analysis runs in a single system, teams avoid the operational cost of maintaining a separate graph database. Data does not need to be duplicated or synchronized, and investigations do not require switching tools or query languages. Relationship context remains tightly coupled with time series data, which speeds root cause analysis and shortens mean time to resolution.

This model supports more effective day-to-day operations. Alerting can reflect real dependencies instead of isolated thresholds. Anomaly detection improves when signals are evaluated in context. Impact assessment becomes clearer as teams can see which components are affected and how far changes propagate. As systems grow more interconnected, this approach helps teams maintain visibility, reduce blind spots, and respond with confidence.

Relational analysis inside a time series engine

InfluxDB 3 enables graph-style analysis by representing system relationships directly in relational tables and querying them alongside time series data. Rather than introducing a separate graph database, teams define structure through schema design and use SQL to navigate relationships across time-stamped events.

Encoding Relationships Through Schema Design

The process starts with schema design. Teams define tables that represent the core entities in their environment, such as services, machines, devices, or users. These entities function like nodes in a graph. Relationships are captured as columns that reference other entities, which serve as the connections between them.

For example, a service record may include a database identifier, or a sensor record may reference the machine it belongs to. These relationships are written into the data model, which makes them directly queryable later. By encoding structure at write time, relationship context stays aligned with incoming metrics and events.

Check out InfluxDB schema design recommendations for more information.

Navigating Dependencies with SQL JOIN

Once relationships are encoded, SQL JOIN provides the traversal mechanism. InfluxDB 3 supports ANSI SQL, allowing teams to join tables using the relationship columns defined in their schemas.

A single JOIN connects related entities. Multiple JOINs allow traversal across several dependency layers, such as from a service to its database and then to the underlying infrastructure. Because time series tables include timestamps, these joins correlate relationships with events, enabling analysis of how changes propagate over time.

This approach supports dependency-aware queries, upstream and downstream impact analysis, and blast radius evaluation using standard SQL.

Executing Relational and Time Series Queries Together

All queries run inside a single execution environment. InfluxDB 3 stores time series data, relational data, and metadata in the same engine and executes queries through a unified SQL layer. The query planner optimizes joins, filters, and scans across all table types without requiring external pipelines or data movement.

Because relational and time series analysis share the same execution path, teams can correlate metrics, events, and dependencies within a single query, keeping analysis tightly connected to the underlying data.

Turning system data into actionable insights

Tracking metrics over time may show changes, but understanding system behavior requires knowing how components connect and how those changes propagate. When structure and timelines stay separate, teams are forced to fill gaps during investigations and planning.

InfluxDB 3 brings relationships and time-stamped data into the same analytical view. Using schema design and SQL JOIN, teams can follow dependencies and analyze cascading events without introducing a separate graph database. This keeps context close to the data, reduces blind spots, and supports clearer decisions as systems grow more interconnected.

Get started with InfluxDB 3 to explore relational analysis on time series data and see how connected insight changes the way you understand and operate complex systems.

Configuring the Alerting Plugin in InfluxDB 3

Allyson Boate (InfluxData) — Tue, 09 Dec 2025 08:00:00 +0000

Monitoring starts with data, but action depends on timely alerts. When an alerting workflow relies on scheduled queries or external checks, engineers miss short windows where values shift and conditions form.

The alerting plugin closes that gap by evaluating alert rules inside InfluxDB 3 as new values arrive, enabling faster detection and more responsive monitoring.

In this tutorial, we’ll walk through configuring the alerting plugin for InfluxDB 3, defining alert logic that evaluates incoming time series data, and emitting alert events that other systems can process. We’ll start by enabling the plugin and creating a basic threshold rule, then move to a short-window evaluation that reacts to patterns rather thanf single points. You’ll generate test data, inspect outputs, and confirm that alert conditions evaluate in real-time. By the end, you’ll have an alerting workflow that you can adapt to your metrics and environments.

How it works

The alerting plugin evaluates each new value as it enters InfluxDB 3. After you define a rule that specifies the condition you want to detect, the plugin compares each incoming point against that rule. When the condition is met, the plugin emits a structured alert event that downstream systems can act on to trigger notifications, automate responses, predict events, or feed a broader event-driven workflow.

Inside InfluxDB 3, the plugin subscribes to the ingestion path and receives points as they arrive. For windowed rules, it maintains a small in-memory buffer of recent values to compute statistics such as an average, minimum, or delta. This makes it possible to detect short-term patterns—like sudden increases or persistent shifts—that individual points may not reveal. Because evaluation happens inline with ingestion, alerts fire as soon as the condition forms.

When a rule matches, the alert event can also be routed through other plugins for additional processing, such as filtering, enrichment, or forwarding to automation systems. Keeping alert logic close to the data pipeline reduces reliance on scheduled queries or external services and ensures that alerts remain responsive under load.

Getting started

Requirements

To follow this tutorial, you’ll need:

An InfluxDB 3 instance
Access to the plugin directory or plugin configuration path
A dataset to monitor or a way to generate test values
Familiarity with writing and querying time series data in InfluxDB 3
A terminal for running example commands

If you prefer to test the alert rules with synthetic values, you can use any script or CLI tool that writes data to InfluxDB 3 at a regular interval.

Step 1: Enable the alerting plugin

Start by confirming that your InfluxDB 3 Core or InfluxDB 3 Enterprise installation has access to the plugin configuration directory. Each plugin loads through a configuration file that specifies how to initialize the plugin and how it should integrate with the data pipeline.

Create a new configuration file for the alerting plugin, or update an existing one, to include the block below:

[plugins.alerting]

  enabled = true

  config_path = "/etc/influxdb3/plugins/alerting/config.yaml"

This block instructs InfluxDB 3 to load the alerting plugin at startup and to read rule definitions from the specified configuration file. After saving these changes, restart your InfluxDB 3 instance so the plugin loads and registers itself with the plugin pipeline.

If you’re new to the plugin ecosystem, enabling the alerting plugin follows the same pattern as other plugins that extend the data pipeline, such as downsampling or forecast error evaluator plugins. Each plugin uses a consistent model for configuration, registration, and integration with the ingestion path.

Step 2: Create a basic threshold rule

With the plugin enabled, we’ll next define a basic alert rule. A threshold rule checks whether an incoming value crosses a limit you care about, such as CPU usage rising above a set percentage. Rules live in the configuration file referenced in the plugin block you created earlier.

Below is a simple example that watches a cpu_usage field and triggers an alert if the value goes above 90:

rules:

  - id: high_cpu

    description: "CPU usage above 90 percent"

    measurement: "system_metrics"

    field: "cpu_usage"

    condition: "value > 90"

In this rule:

measurement and field identify the series to monitor
condition defines the comparison that the alert checks
id helps you track or reference the rule in downstream systems

When the alerting plugin loads this file, it parses the condition expression and registers it as part of the rule evaluation engine. Each time a new data point arrives for the matching measurement and field, the plugin substitutes the point’s value into the expression and evaluates it. If the expression returns true, the rule matches and the plugin emits an alert event.

After adding the rule, save the file so the alerting plugin can load it at startup or during the next configuration reload.

Step 3: Create a short-window rule

Threshold rules work well for single values, but some conditions only appear when you look at how values change over a short period of time. Short-window rules examine a small group of recent points to detect patterns such as spikes, drops, or sustained increases. The alerting plugin keeps this window in memory so it can compute the needed statistics.

The example below monitors temperature readings and triggers an alert when the average value over the last five points rises above 80:

rules:

  - id: rising_temperature

    description: "Average temperature over last 5 points exceeds 80"

    measurement: "sensor_data"

    field: "temperature"

    window:

      size: 5

      statistic: "avg"

    condition: "value > 80"

In this rule:

window.size defines how many recent points to include
window.statistic describes what the plugin should compute (average, minimum, maximum, delta, etc.)
The computed statistic becomes the value used in the condition expression

As each point arrives, the plugin updates the window. Once enough points are available, it computes the statistic and evaluates the condition. If the condition matches, the plugin emits an alert event.

Short-window rules are useful for detecting behavior that individual points may not reveal.

Step 4: Route and view alert events

When a rule matches, the alerting plugin generates a structured alert event. Each event includes the rule ID, the triggering value, the timestamp, and any tags associated with the series. These events move through the plugin pipeline, where they can be logged, forwarded, or processed by other systems.

A simple configuration writes alert events to a local log file:

[plugins.alerting.outputs.log]

  enabled = true

  path = "/var/log/influxdb3/alerts.log"

Logging is the quickest way to verify that a rule fires correctly. Each alert is written in a structured format so you can check the rule ID and the value that triggered it.

After confirming rule behavior, you can route alerts to systems that process events or trigger automation.

If you want alerts to flow into an event-processing system, you can send them to a message queue such as Kafka:

[plugins.alerting.outputs.kafka]

  enabled = true

  brokers = ["localhost:9092"]

  topic = "alert_events"

To trigger notifications or automation tools through an HTTP endpoint, such as Webhook:

[plugins.alerting.outputs.webhook]

  enabled = true

  url = "https://example.com/alerts"

  method = "POST"

You can also forward events to custom plugins or internal consumers when you need domain-specific logic. Because alert events include rule metadata and series tags, downstream systems can filter or enrich them before taking action.

Choosing an Output Format

Different outputs serve different purposes, so it helps to choose the one that matches your workflow:

Logs: Best for development and confirming rule behavior
Kafka: Suited for scalable, asynchronous event processing
Webhooks: Good for triggering automation or notification services
Custom plugins: Ideal for internal logic or specialized processing
Multiple outputs: Enable more than one if you need alerts to reach both dev and production targets

Write a few test points during development to confirm that alerts appear where you expect them.

Step 5: Validate rule behavior with test data

Before using an alert rule in production, validate that it behaves as expected by writing controlled test data into InfluxDB 3. This confirms that the rule targets the correct measurement, field, and condition.

To test a threshold rule, write a value that intentionally exceeds the limit:

influx write \

  -b system_metrics \

  system_metrics cpu_usage=95

If the rule is configured correctly, the plugin should emit an alert event in your chosen output.

For windowed rules, write a short sequence of values that move the computed statistic toward the condition. For example, if the rule evaluates the average of the last five temperature readings:

influx write -b sensor_data sensor_data temperature=75

influx write -b sensor_data sensor_data temperature=78

influx write -b sensor_data sensor_data temperature=82

influx write -b sensor_data sensor_data temperature=85

influx write -b sensor_data sensor_data temperature=88

After the final point, the window should meet the rule’s condition and fire an alert.

Verify that:

the alert appears in your output target
the rule ID matches the rule you tested
the triggering value is correct

Testing with synthetic data helps ensure rules fire when expected and reduces noise as you move into production.

Best practices for alert rules

A few tips to help keep alerting accurate and reduce noise:

Target the right series using tags and specific fields.
Test rule sensitivity with controlled data before production.
Mix threshold and windowed rules to catch both spikes and gradual changes.
Keep window sizes small so alerts respond quickly.
Choose outputs that match your workflow, such as logs for development or queues/webhooks for production.
Enable multiple outputs if you want alerts to flow to both development and production destinations.

Next steps

The alerting plugin adds real-time rule evaluation to InfluxDB 3, helping you detect important changes as data arrives. With threshold and windowed rules, flexible routing options, and a straightforward way to validate behavior, you can build an alerting workflow that supports timely responses to shifting system conditions.

Ready to get started?

Review Getting Started Guide for InfluxDB 3 Core and Enterprise to continue building your workflow.
Explore the InfluxDB 3 plugin ecosystem to extend your data pipeline with additional processing, routing, and automation capabilities.
Share feedback or questions with the development team on Discord (#influxdb3_core), Slack (#influxdb3_core), or the Community Forums.

Using the Downsampling Plugin in InfluxDB 3

Allyson Boate (InfluxData) — Tue, 02 Dec 2025 08:00:00 +0000

Modern systems generate huge volumes of time series data. Advances in hardware and edge instrumentation enable sensors and applications to capture new values every second—or faster—which makes high-frequency measurement easy and affordable. When applied effectively, this steady flow of data reveals early warning signs, highlights subtle performance shifts, and helps teams understand how systems behave in real-time.

Volume, however, rises fast. As systems expand and more signals come online, incoming metrics grow at a pace that pushes dashboards, queries, and storage beyond their limits. The same data that strengthens observability can place increasing strain on the tools meant to interpret it. Teams need more than raw detail; they need a clear view of how values change over time and a system that can keep up with the data it collects.

When data growth slows systems

As high-frequency data grows, the strain shifts from individual queries to the entire monitoring workflow. Dashboards take longer to refresh because they must scan large time ranges. Queries require more computing, which increases cloud spend and slows routine investigations. Storage fills sooner than expected, often forcing earlier retention cuts or more aggressive archiving.

These slowdowns carry a tangible business impact. Delayed dashboards mean engineers spot issues later, which increases the risk of prolonged incidents or missed early indicators. Higher compute usage drives up operational costs, especially for teams managing large-scale cloud deployments. Shortened retention removes the historical context needed for forecasting and capacity planning, leading to decisions based on partial or incomplete information. Over time, organizations pay more for monitoring while seeing less.

Taken together, these pressures limit how effectively teams can investigate anomalies, validate system health, and track behavior across longer time windows. Reduced visibility lowers confidence in day-to-day operations and slows the ability to act when systems show early signs of change.

Organizations need a long-term strategy that keeps datasets manageable, preserves signal quality, and maintains visibility as systems scale.

What downsampling is

Downsampling reduces the volume of time series data by grouping older points into wider intervals and summarizing them with aggregates. Instead of storing every reading, the system creates buckets—such as 30 seconds, 1 minute, or 5 minutes—and computes values like averages, minimums, or maximums for each bucket. Recent data remains at full resolution while older data becomes lighter and easier to work with.

This structure supports multi-resolution storage: short windows stay detailed, mid-range summaries support operational analysis, and longer intervals preserve the trends needed for multi-day insight. The result is a clearer dataset that scales without overwhelming dashboards, compute resources, or storage.

Downsampling makes long-term time series analysis sustainable. By transforming high-frequency values into structured summaries, teams gain a clearer view of patterns that emerge over hours, days, or weeks. Trend lines become easier to compare, multi-day windows load faster, and queries stay efficient even as telemetry grows. This approach gives organizations a reliable foundation for forecasting, capacity planning, and understanding how behavior changes over extended periods.

Downsampling in action

Consider vibration sensors mounted on industrial pumps. Each sensor records a new amplitude every second to detect early signs of mechanical wear. A single device generates more than 80,000 readings per day. A fleet of 250 sensors produces more than 20 million points daily. Querying a 30-day window of raw data forces dashboards to scan hundreds of millions of rows, slowing analysis and increasing compute cost.

Converting 1-second vibration readings into 1-minute rollups reduces data volume by more than 98% while preserving the overall vibration pattern. Engineers still see changes in amplitude, identify spikes, and compare trends across pumps, but queries return faster, and storage consumption remains manageable.

This same pattern benefits application latency metrics, infrastructure telemetry, and IoT data streams. Downsampling connects these layers so systems stay responsive in real-time while maintaining a clear historical record.

The downsampling plugin in InfluxDB 3

The downsampling plugin in InfluxDB 3 automates the entire rollup process. Instead of maintaining custom scripts or recurring SQL jobs, teams configure a small set of rules that the plugin applies on schedule. The plugin reads the source table, groups older values into defined time buckets, computes aggregates, and writes the results into a dedicated summary table. Because InfluxDB 3 stores data in a columnar format, these operations run efficiently even at a large scale. Teams gain consistent summaries without changing how they write queries or manage retention.

Using the downsampling plugin: A step-by-step guide

Once you understand why downsampling matters, the next step is to turn that understanding into a reliable workflow. This tutorial walks through how to identify the right measurement to summarize, create a rollup table, configure the plugin, and confirm that the summarized values reflect the underlying time series data. The goal is simple: keep insight high and long-term data manageable as systems scale.

Before You Begin

You will need an InfluxDB 3 instance, a measurement that receives high-frequency time series data, and access to run SQL. This guide assumes a raw measurement, such as metrics_raw, that records values at least once per second.

1. Identify the data that needs downsampling

Not every measurement benefits from summarization. The best candidates are those that grow quickly and slow down long-range queries or dashboards. A quick count over a single day helps reveal how quickly a dataset expands:

SELECT COUNT(*)
FROM metrics_raw
WHERE time >= now() - interval '1 day';

If the result shows millions of points, downsampling will likely improve performance. Measurements with second-level or sub-second readings usually generate the highest volume and offer the clearest benefit.

2. Clarify your rollup goals

Before building any rollups, decide how much detail you need for long-term analysis. Downsampling works best when intervals match the time ranges your team uses. A 1-minute view is typical for operational dashboards, while 5- or 15-minute summaries help with trend analysis. Wider intervals, such as 30 minutes or an hour, support multi-day or multi-week reviews.

Aggregates define what the summary represents. AVG() captures overall direction, MIN() and MAX() preserve the range of behavior, and COUNT() confirms sample density. The right mix keeps meaningful variation while reducing noise and storage.

3. Create a table for summarized data

Rollups work best when written to their own table, separate from the raw measurement. This keeps data organized and makes it easier to query the correct layer for each use case. Use the SQL time_bucket function to group older points into predictable intervals that match your rollup needs.

CREATE TABLE metrics_1m AS
SELECT
  time_bucket(INTERVAL '1 minute', time) AS bucket,
  device_id,
  AVG(value) AS avg_value,
  MIN(value) AS min_value,
  MAX(value) AS max_value,
  COUNT(*) AS samples
FROM metrics_raw
GROUP BY bucket, device_id;

This reduces 1-second readings to 1-minute summaries. The trend remains clear, but the volume becomes far easier to work with.

4. Configure the downsampling plugin

With the rollup table in place, the downsampling plugin automates the summarization process. The plugin reads new points from your raw table, applies the defined interval and aggregates , then writes updated summaries to the rollup table.

You can configure and schedule this job directly through the InfluxDB 3 plugin library, where each plugin includes its own setup options.

Most teams schedule the plugin to run every few minutes to keep dashboards fresh. A five-minute schedule works well for 1-minute rollups, while 15-minute or hourly schedules work for wider intervals. Each run processes only new data, which keeps the workload efficient even as datasets grow.

Once configured, the plugin maintains the entire pipeline without manual cleanup or recurring scripts.

5. Direct queries to the right data layer

Once rollups are in place, you can query data with SQL against the raw or summarized tables, depending on the time range you need to explore. Raw time series data is ideal for short-term investigation, but long-range dashboards benefit from summarized data. After rollups are in place, point dashboards and analytics tools to the rollup table for wider windows. This reduces the number of rows scanned and makes charts more responsive.

A typical pattern looks like this:

Last hour uses raw data
Last 24 hours uses 1-minute rollups
Multi-day or monthly windows use 5-minute or hourly rollups

Here’s how the difference plays out in practice.

Raw data:

SELECT time, device_id, value
FROM metrics_raw
WHERE device_id = 'sensor-101'
  AND time >= now() - interval '30 days';

Rollups:

SELECT bucket AS time, device_id, avg_value
FROM metrics_1m
WHERE device_id = 'sensor-101'
  AND bucket >= now() - interval '30 days';

The rollup query is lighter, faster, and more predictable at scale.

6. Validate your summaries

Before fully relying on rollups in production, confirm that your summarized values reflect the raw measurement. Compare both layers over a small window:

-- Raw data
SELECT MIN(value), MAX(value), AVG(value)
FROM metrics_raw
WHERE time >= now() - interval '1 hour';

-- Rollups
SELECT MIN(avg_value), MAX(avg_value), AVG(avg_value)
FROM metrics_1m
WHERE bucket >= now() - interval '1 hour';

The values should align closely. If results fall outside expected ranges, check the job schedule or confirm that your buckets line up with the raw timestamps.

7. Build additional rollup layers if needed

Some teams need views that span weeks or months. In these cases, multiple layers of rollups help maintain fast queries without sacrificing clarity. A common structure moves from 1-second data to 1-minute summaries, then to 5-minute summaries, and finally to hourly summaries for long-term storage.

Use these layers only when your dashboards require them. Too many layers can add more complexity than value.

8. Putting the workflow in place

Once you configure the downsampling plugin, InfluxDB 3 maintains a clear data lifecycle. High-frequency data stays at full resolution for recent troubleshooting. Summaries provide efficient visibility across longer windows. Dashboards load quickly, queries run with less compute, and storage grows at a predictable rate. The workflow stays consistent as systems scale.

Optimizing data with downsampling

As time series volume grows, so does the cost of storing and querying it. Enterprises feel this impact in higher compute usage, slower analysis, and retention strategies that no longer align with operational needs. Downsampling gives teams a reliable way to control these costs by shaping data into structures designed for long-range visibility and efficient retrieval. With automated rollups in place, organizations reduce the overhead of wide-window queries and build monitoring workflows that stay responsive as environments expand.

See how downsampling fits into a cost-efficient monitoring strategy. Get started with a free download of InfluxDB 3 Core OSS or a trial of InfluxDB 3 Enterprise.

The High Stakes of Aerospace Reliability

Allyson Boate (InfluxData) — Thu, 13 Nov 2025 08:00:00 +0000

Aerospace systems operate in one of the most unforgiving environments imaginable. Each flight test, orbital maneuver, or satellite transmission subjects avionics, propulsion systems, sensors, and telemetry hardware to extreme conditions. Even a minor failure can cascade into grounded aircraft, interrupted communications, or compromised missions. The operational and financial implications are massive: a single day of downtime for a major airline or a disrupted satellite feed can cost hundreds of thousands of dollars.

Organizations across aviation and aerospace invest heavily in maintenance, monitoring, and repair to keep assets fully operational. Yet much of that effort still goes toward reactive repairs or early part replacements that could be avoided with better insight. As aerospace programs modernize and budgets tighten, predictive maintenance and real-time telemetry monitoring offer a clear advantage. By using time series data and machine learning (ML), teams can identify early signs of wear before failure occurs, enhancing safety, improving efficiency, and extending component life.

Traditional maintenance models focus on compliance rather than optimization. Predictive maintenance breaks this model by using real-time performance data to anticipate system behavior before reliability is at risk. With the proper data infrastructure, teams move from reactive response to proactive precision.

Outgrowing traditional maintenance

Reactive maintenance only responds after something breaks. Preventive maintenance replaces parts on a schedule, often long before they’re needed. Neither approach captures how components behave under constantly changing flight conditions.

Modern aircraft generate vast amounts of telemetry data each flight, including temperature, vibration, torque, and current. Scaled across global fleets, airlines multiply this already massive volume of time series data every day. Yet legacy systems cannot process or analyze that data quickly enough to identify trends as they emerge. Without early detection, opportunities for intervention disappear.

Consider an airline that notices recurring temperature spikes in a particular engine type. Individually, each reading looks minor. Viewed over time, those spikes reveal a pattern: temperature increases followed by rising vibration levels several hours before bearing wear begins. Without a high-performance time series database, that pattern remains hidden until a part fails mid-route. The result is unscheduled maintenance, rerouted flights, and preventable costs.

Predictive maintenance depends on continuously capturing and analyzing data. To do that, aerospace organizations need a time series platform that scales, processes, and visualizes sensor data as it’s generated.

Predictive maintenance with time series and ML

Predictive maintenance turns continuous telemetry into foresight. Instead of reacting to failures, teams monitor performance data that shows how systems behave during every stage of flight. With time series data and machine learning (ML), organizations gain a living picture of aircraft health that evolves in real-time.

Continuous Monitoring

Every flight generates millions of data points on temperature, vibration, pressure, and electrical current. These readings flow directly into a centralized time series database, where they’re stored, organized, and time-stamped for instant retrieval.

Continuous visibility lets engineers see how components perform in real-world conditions, rather than relying on averages or inspection intervals. Over time, the data builds a precise operational fingerprint for each asset, capturing how it reacts to altitude, load, and environmental change. This baseline is the foundation for identifying performance drift long before it causes disruption.

Anomaly Detection

Once the data is centralized, ML algorithms evaluate it against those established baselines. Rather than flagging single outliers, the models look for patterns that indicate gradual degradation: a subtle increase in vibration, a minor power fluctuation, or a slow temperature rise. Individually, these shifts may seem harmless; together, they signal an emerging issue.

This pattern-based approach enables maintenance teams to detect small but consistent deviations that traditional inspection cycles would miss. It also helps prioritize what matters most by filtering out false alarms, keeping attention on trends that truly affect reliability and safety.

Proactive Response

When an anomaly indicates a developing issue, the system automatically generates an alert. Maintenance teams can then assess the risk, plan corrective action, and align it with scheduled service windows. Instead of grounding an aircraft unexpectedly, they can replace or recalibrate parts during routine downtime.

As the system captures more data, these responses refine the underlying models. Each confirmed case—whether genuine fault or false alarm—teaches the algorithm to better interpret new signals. The result is a feedback loop that grows more accurate over time, reducing unnecessary interventions and improving fleet availability.

How It All Works Together

Time series data provides the full context that ML needs to make sense of change: how vibration, temperature, and efficiency shift together under specific flight conditions. Techniques such as regression, classification, and anomaly detection transform that raw telemetry into predictive insight.

InfluxDB 3 underpins this process. Its columnar storage, high-ingest performance, and native support for Python-based analytics make it possible to process billions of data points quickly and feed results back into active ML workflows. The platform scales seamlessly from individual aircraft systems to entire fleets, ensuring that predictive maintenance insights remain timely, accurate, and actionable.

From sensor to insight: the predictive workflow

Every flight generates a constant flow of sensor data. InfluxDB 3 structures telemetry into a workflow that converts information into intelligence and intelligence into action.

Ingestion

Thousands of sensors capture flight conditions in real time—temperature, pressure, current, and vibration, among them. This high-frequency data streams into InfluxDB 3 through Telegraf agents and native ingestion APIs, where each point is time-stamped and indexed for immediate access. The result is a unified, scalable foundation that supports continuous monitoring and detailed trend analysis.

Processing

Once collected, data must be cleaned and prepared. InfluxDB 3 enables engineers to remove noise, normalize readings, and extract key features such as vibration frequency shifts or temperature gradients using Python-based processing. These in-database transformations simplify workflows by reducing the need for external tools, revealing subtle performance changes, and making the data immediately usable for ML training.

Model Training

Machine learning frameworks can connect directly to InfluxDB 3 via Python processing and SQL-based queries. Models train on historical data to identify signatures that precede maintenance events—such as gradual heat buildup, torque imbalance, or changing vibration patterns. This direct connection shortens iteration cycles and builds an evolving understanding of each component’s behavior.

Real-Time Detection

Deployed models continuously evaluate live data streams from InfluxDB 3. By comparing current behavior against learned baselines, they detect small but consistent deviations that indicate developing faults. Engineers can automate alerting and response through integrations with Grafana, MQTT, or other analytics systems, gaining early insight before downtime occurs.

Feedback and Retraining

Each confirmed maintenance event strengthens the model. Data from repairs and inspections feeds back into InfluxDB 3, helping the system learn which patterns reliably predict true performance drift. The more it learns, the more precise its recommendations become.

This self-improving loop turns raw sensor data into a continuously evolving intelligence system that enhances reliability with every flight.

Real-world case study: predictive maintenance in orbit

For Thales Alenia Space, predictive maintenance is essential. The company designs and operates satellite systems for communication, navigation, and Earth observation, missions where in-flight repairs are impossible and reliability depends on data.

Each satellite transmits continuous telemetry on temperature, vibration, current, and structural load. Using InfluxDB 3, Thales Alenia Space ingests and analyzes this data in real time and during post-mission review. High-ingest performance and precise querying enable engineers to detect slight variations that indicate wear or stress before they become failures.

Machine learning models trained on historical time series data identify patterns of drift across subsystems. When early warnings appear, ground teams can adjust settings, redistribute power, or trigger corrective actions remotely, preventing outages and extending satellite lifespan.

With InfluxDB 3, Thales Alenia Space turns continuous telemetry into a predictive maintenance system that safeguards mission integrity where physical intervention isn’t possible.

Turning data into reliability

Predictive maintenance is transforming how aerospace teams manage performance. Early insight replaces reactive repair, cutting unplanned downtime and extending component life. Maintenance becomes proactive and efficient, guided by real-time performance data instead of fixed schedules.

Continuous monitoring also reinforces safety and compliance. Each component’s performance record updates automatically, giving engineers a verified trail that meets operational and regulatory standards. At scale, InfluxDB 3 delivers this visibility across fleets without lag or data loss.

These operational gains support broader goals. More innovative maintenance planning reduces waste, conserves parts, and lowers energy use by ensuring work happens only when needed. The result is a more predictable and sustainable operation built on continuous insight.

With InfluxDB 3 as the foundation, aerospace organizations gain the speed and intelligence to keep systems performing at their best, both in the air and on the ground.

The next era of aerospace intelligence

Aerospace is advancing toward autonomous maintenance, where aircraft monitor and optimize themselves in flight. Powered by AI and real-time analytics, onboard systems will interpret sensor data, detect anomalies, and act before faults occur. As edge computing evolves, this analysis moves closer to the source, allowing aircraft to process telemetry locally and reduce manual intervention.

Digital twins, virtual models of real-world systems, replicate aircraft systems in real time. With continuous updates, these virtual models will simulate wear, forecast performance, and guide maintenance decisions before physical service is required. Each cycle strengthens model accuracy, creating a feedback loop that enhances reliability across fleets.

InfluxDB 3 forms the foundation for this evolution. By connecting edge analytics with centralized intelligence, it keeps predictive and autonomous systems accurate, adaptive, and scalable—enabling a future where data prevents failure entirely.

The verdict

Predictive maintenance marks a fundamental shift in how aerospace organizations think about reliability. By combining time series data and ML, maintenance evolves from reactive repair to proactive assurance. Start transforming your maintenance strategy today for aerospace and beyond. Get a free download of open source InfluxDB 3 Core or a trial of InfluxDB 3 Enterprise.