Future-Proofing Your Historian with a Time Series Database

By Allyson Boate / Developer
Sep 17, 2025

As technology scales and data volumes accelerate, organizations face a pressing challenge: how can they modernize data infrastructure without putting daily operations at risk? Data historians, specialized databases that capture and store time-stamped machine and sensor data, have long been the foundation for reliability and compliance. However, they were not designed for the openness and advanced analytics that modern workloads demand. Replacing them outright is costly and disruptive, while standing still leaves teams with limited visibility and rising integration costs.

The solution? Augmentation. By adopting parallel data pipeline architectures, organizations can run InfluxDB alongside existing historians. In practice, this means streaming data into both systems at the same time. The historian continues to serve as the system of record, while InfluxDB provides real-time visibility, scalable storage, and advanced analytics. Businesses gain modern capabilities without disrupting the systems they already depend on.

Why organizations need to evolve

Companies rely on time series data, collected at regular intervals from machines and sensors, to guide operations, forecast issues, and maintain compliance. When teams cannot act on this information in real-time, they face delays, rising costs, and inefficiencies. A time series database stores and organizes these readings, turning raw inputs into insights that support faster decisions and more accurate predictions. Without updates, performance lags and opportunities are missed. The need for change is clear, but the real challenge is making it without disrupting daily operations.

Common Challenges of Modernization

When teams attempt to modernize a historian, they can create new obstacles instead of solving old ones. Full replacements may introduce more features; however, they require major investment, carry operational risk, and cause downtime that threatens compliance and revenue. Custom integrations send historian data into other systems with fragile and expensive-to-maintain connectors. Bolt-on analytics add dashboards on top of a historian but arerestricted by closed architectures and limited scalability. Data exports suffer from delays and inconsistencies that slow down response and limit accuracy.

Consider a manufacturing plant that relied on siloed historians across multiple sites. Their data was fragmented, making it difficult to track performance across facilities. Reconciling information manually wasted time, energy, and money, so leaders pushed to update their systems. They attempted a full migration to a new platform, hoping for a unified view. However, the project required retraining staff, rewriting integrations, and pausing critical workflows. These changes resulted in costly delays, compliance risks, and resource strain. The wrong approach consumed capacity without addressing the central challenge: how to evolve incrementally while maintaining stable production.

Augmenting instead of replacing

Updating a data strategy doesn’t have to mean a complete overhaul or choosing between a historian and a time series database. Instead, companies can extend the value of their historians by utilizing parallel data pipeline architectures, where information flows into both the historian and the time series database. The historian provides a stable system of record, while the time series database complements it by making the same information available in real-time for analytics and dashboards. This method limits disruption, avoids the expense of full replacement, and resolves the shortcomings of fragile integrations or delayed batch exports.

Why Both Systems Matter

Some may ask whether using both a historian and a time series database is redundant. In reality, combining the two saves money and reduces risk. The historian ensures compliance, stability, and integration with OT systems, while the time series database delivers real-time analytics, scalability, and modern integrations. Running them together is not duplication, but a cost-conscious strategy that balances reliability with innovation.

A leader in the energy field used parallel pipelines to update operations. Substation data continued to flow into the historian, ensuring compliance and stable reporting, while a time series database delivered the same data in real-time through dashboards and alerts. By augmenting their historian rather than replacing it, operators gained both the reliability of their existing system and the agility of real-time analytics. OT operations leaders, who oversee SCADA systems, real-time data acquisition, grid monitoring, and industrial control systems (ICS), ensured seamless integration and data consistency across both platforms. This allowed them to anticipate outages, fine-tune performance, and schedule maintenance proactively. The outcome was fewer disruptions, more reliable service, and measurable cost savings.

Building parallel pipelines

Parallel pipelines are best understood as a set of building blocks that work together. The goal is to let information flow into both the historian and a secondary platform at the same time, maintaining compliance while enabling real-time analysis. Three core techniques support this design: simultaneous ingestion, stream processing, and data validation.

Simultaneous Ingestion

Simultaneous ingestion sends the same stream of data to both the historian and the secondary platform simultaneously. Compliance systems continue to capture and store a trustworthy record, while analytics systems receive identical information in real-time. Connectors such as OPC UA or MQTT handle this duplication without introducing drift.

• Best used for: Environments that require compliance records and real-time analytics from the same data stream.
• Considerations: Test throughput under peak load so ingestion tools do not create bottlenecks.
• Further information: Check out the Telegraf Input Plugins documentation for details on setup and monitoring.

Stream Processing

Stream processing analyzes and transforms data in motion rather than waiting until it is stored. This approach reduces lag and helps teams respond to events as they occur. Platforms such as Kafka, Flink, or Telegraf processors filter, enrich, or route information before it lands in storage.

• Best used for: Live dashboards and event-driven alerts that depend on immediate insight.
• Considerations: Too many transformations can add latency, so keep them lightweight and targeted.
• Further information: Explore Apache Kafka for tutorials and examples on designing efficient pipelines.

Data Validation

Data validation ensures that the information in the historian remains consistent with the data stored in the secondary platform. This step builds trust by confirming that compliance data and analytics outputs stay aligned. Teams run scheduled or continuous queries across both systems to compare values and timestamps. Automated checks surface discrepancies so they can be resolved quickly.

• Best used for: Ensuring that compliance records and analytics outputs remain aligned.
• Considerations: Assign responsibility for handling mismatches promptly, so confidence in the data is not eroded.
• Further information: See the InfluxData query documentation for guides on creating and automating comparisons.

Best Practices for Reliable Pipelines

It is best to start with a small set of critical data flows to prove that the pipeline works as intended, then scale once stability is clear. Documenting the configuration of ingestion and processing helps prevent configuration drift as systems grow. Real-time monitoring should be in place to allow observation of both platforms and surface issues early. Clear ownership of data validation ensures that discrepancies are handled quickly rather than undermining trust. Integration points should be reviewed regularly, with connectors and settings updated as conditions evolve.

The takeaway

Parallel data pipelines offer a practical way for organizations to evolve without discarding systems that still serve them well. Running both platforms in tandem delivers scalability, flexibility, and real-time insight, while preserving the compliance and reliability that historians are known for. This balance reduces risk, lowers costs, and creates a stronger foundation for future growth.

Ready to take the next step? Watch the webinar Getting Started with InfluxDB 3. Or, start exploring InfluxDB 3 today with a free download of Core OSS or a trial of Enterprise.