Data Engineering for Enterprise and Industrial Data Platforms

Short answer. AgenticNex AI designs and builds batch and streaming data platforms that make enterprise and operational data usable for analytics and AI. We integrate ERP, EAM, SCADA, historians, sensors and documents into governed pipelines with quality checks, lineage and access control.

What is the business problem?

Operational data sits in historians, PLCs, ERP and spreadsheets, each with its own formats, timestamps and owners. Analytics teams spend most of their time finding and cleaning data.

Why can conventional approaches be insufficient?

  • Nightly batch exports are too slow for operational decisions.
  • Point-to-point integrations break whenever a source changes.
  • No lineage, so nobody can say which data a report or model used.

What is our engineering approach?

  1. Map sources, owners, latency needs and quality issues.
  2. Build ingestion for streaming and batch on open protocols.
  3. Model data into curated layers with tests, lineage and access rules.
  4. Operate pipelines with monitoring and alerting.

What does the architecture look like?

Sources: SCADA · historians · ERP · EAM · filesIngestion: OPC UA · MQTT · CDC · APIsStreaming: Kafka / FlinkLakehouse: raw → curated → servingQuality, lineage & catalogueAnalytics · ML · GenAI consumers
Data Engineering reference architecture. Sources: SCADA · historians · ERP · EAM · files → Ingestion: OPC UA · MQTT · CDC · APIs → Streaming: Kafka / Flink → Lakehouse: raw → curated → serving → Quality, lineage & catalogue → Analytics · ML · GenAI consumers

Which technology capabilities are involved?

  • Apache Kafka and Apache Flink
  • Spark and lakehouse formats (Delta / Iceberg)
  • OPC UA, OPC DA bridges, MQTT, WITS/WITSML
  • dbt-style transformations and data tests
  • Data catalogue and lineage
  • Azure, AWS and on-premise

Example use cases

  • Unifying historian and maintenance data for asset analytics
  • Real-time ingestion of rig or plant telemetry
  • Data platform modernisation from legacy warehouses
  • Data cleaning and validation for regulatory reporting

Expected business and technical outcomes

  • Trusted, documented data for analytics and AI
  • Lower latency from event to insight
  • Fewer brittle point-to-point integrations
  • [Insert verified project result]

Relevant case studies

Case study 02

Railway — Asset Knowledge Graph

Case study details coming soon

Frequently asked questions

Can you integrate legacy OPC DA systems?

Yes. We build OPC DA to OPC UA bridges so legacy plant data can stream into modern platforms.

Batch or streaming?

Both. The choice is made per source and use case, based on the latency the decision actually needs.

Related insights

Architecture Guides

Real-Time AI/ML Analytics Architecture

A real-time AI/ML analytics architecture ingests sensor and event streams at the edge, processes them in a stream engine such as Apache Flink, computes features in event-time windows, runs model inference on the stream and delivers calibrated alerts to operators within seconds.

Industry

Water & utilities

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