Perspectives on Industrial Data Systems
Technical perspectives on data architecture, edge execution, and the realities of working with industrial systems.
First-Mile Data Challenges
The Fragmentation Tax: What Protocol Diversity Actually Costs
Every industrial facility runs multiple protocols. The cost of bridging them without a structured data layer is higher than most organizations measure.
DNP3 at the Edge: Bringing Utility SCADA into the Unified Namespace
DNP3 is the dominant protocol in utility SCADA, yet most IIoT platforms treat it as an afterthought. Bringing DNP3 data into a structured namespace requires understanding polling, unsolicited reporting, and the realities of substation connectivity.
Architecture & Data Modeling
Unified Namespace: Implementation Realities Beyond the Theory
UNS is an architectural pattern, not a product feature. Implementing it correctly requires solving data acquisition, contextualization, and delivery - not just topic hierarchy design.
Structure Data Before It Reaches the Lake
Sending raw telemetry to a data lake and structuring it later is an anti-pattern. Data must carry context from the point of origin.
From Edge to Historian: Why Structure Before Storage Matters for Time-Series Data
Time-series databases are natural historians for industrial data. But raw telemetry without structure creates the same problems in InfluxDB or TimescaleDB as it does in a data lake. Structure at the first mile determines historian value.
SparkplugB and the Unified Namespace: Complementary, Not Competing
SparkplugB standardizes MQTT-based industrial communication. The Unified Namespace structures all industrial data. They solve different problems - and work best together.
Edge Execution & Pipelines
Why Deterministic Pipeline Behavior Matters in Industrial Systems
Industrial data pipelines must produce the same output given the same input, with bounded latency. Non-deterministic behavior in the data path creates operational risk.
Durable Buffering: Why Store-and-Forward Is Not Optional at the Edge
Network reliability in industrial environments is a myth. Any data plane that does not buffer locally and replay on reconnection will lose data.
Platform & Strategy
Control vs Integration: Why Middleware Is Not a Data Plane
Integration platforms move data between systems. A data plane structures what data exists and how it behaves. These are fundamentally different architectural roles.
Scaling a Data Plane Across Multiple Sites
Multi-site deployments require consistent data structure, centralized fleet management, and deployment orchestration. Scaling is an architectural problem, not a capacity problem.
S3, GCS, and Iceberg: Delivering Industrial Data to the Lakehouse
Industrial organizations increasingly operate across cloud providers. A data plane that structures data at the edge and delivers it in open formats decouples industrial data from cloud vendor lock-in.
Explore how these concepts apply to your environment
Discuss deployments, data architecture, or how to establish first-mile data control in your facilities.