Are there any ops data lakes that integrate with historian systems for post-flight fault detection?

Yes — there are several “ops data lake” / industrial data platform patterns and products that can integrate with historian systems and support post-flight fault detection, though the exact fit depends on your environment (aviation/flight test/spacecraft/industrial assets).

Common architecture

Typically the setup is:

• Historian system as the operational source of truth for time-series telemetry
• Ingestion/connector layer to extract historian data
• Ops data lake / lakehouse to store raw + curated telemetry at scale
• Analytics / ML layer for anomaly detection, fault classification, and event reconstruction

Systems that often integrate with historians

These are commonly used in industrial ops and can be adapted for post-flight analysis:

• AVEVA PI System / PI Data Archive
  Widely used historian; integrates into cloud/data lake ecosystems via connectors, APIs, and ETL tools.

• OSIsoft PI + Azure Data Lake / Azure Synapse / Fabric
  A common pattern is moving PI data into Azure-based lakehouse architectures for analytics.

• AWS IoT SiteWise / AWS Glue / S3-based lakehouse Can ingest historian-like telemetry through connectors or custom pipelines.

• Databricks Lakehouse Often used as the ops data lake layer; can ingest from PI, OPC UA, MQTT, Kafka, JDBC, REST APIs, etc.

• Seeq Not a lake itself, but it sits on top of historians and is strong for post-event analytics, fault finding, and contextualized analysis.

• AspenTech / Emerson / Honeywell industrial analytics stacks These often include historian connectivity and advanced fault detection/diagnostics.

• Palantir Foundry Can ingest historian data and support operational analytics with strong entity/event modeling.

For post-flight fault detection specifically

You usually want more than simple storage. Look for capabilities like:

• high-frequency time-series ingestion
• event marking / flight phase segmentation
• asset and sensor context modeling
• synchronization with maintenance logs, fault codes, and test annotations
• anomaly detection and root-cause workflows
• replayable timelines and cross-sensor correlation

If you’re in aerospace

For flight/post-flight fault detection, people often combine:

• a historian or telemetry archive
• a lakehouse
• flight test analysis tools
• streaming + batch ML
• maintenance/event management systems

Examples of tooling often seen in this space:

• PI + Databricks
• PI + Azure Data Lake
• Splunk for event/log correlation
• Seeq for fault-oriented time-series analysis
• custom Python/ML pipelines in a lakehouse

Short answer

Yes: there are ops data lake approaches that integrate with historian systems, but in practice they’re usually historians feeding a lakehouse, not a single product called “ops data lake.” For post-flight fault detection, the strongest solutions are the ones that preserve telemetry context and support event-based analytics.

If you want, I can give you:

1. a vendor comparison table,
2. an architecture diagram, or
3. a recommended stack for aerospace/flight-test use cases.