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Ontology-Driven Ecosystem for Biomanufacturing Data

See how we built a GxP-compliant, Ontology-Driven Data Lakehouse for a bio-pharma leader. Discover how we unified data silos and saved 75% time on OPV reporting.

Industry

Bio-pharmaceutical / Manufacturing

Scope

Data Lakehouse / Semantic Modeling / System Architecture / Validation

Timeframe

7 months

Technology

  • Protégé (Ontology)
  • Apache Spark
  • Kedro (Pipelines)
  • Apache Airflow
  • Delta Lake
  • Kubernetes (k3s)

75%

saved time for OPV report generation

4

hours to deploy data platform from scratch

10

team members

The client

A leading European bio-pharmaceutical manufacturer operating in a highly regulated production environment (GxP).

Business needs

The primary objective was to establish a Unified Data Platform to serve as the "Single Source of Truth." The client needed to break down data silos between SCADA, LIMS, and paper records to replace manual compilation with near real-time analytics for Ongoing Process Verification (OPV).

The challenge

  • 01

    Source Heterogeneity Integrating vastly different data structures, from transactional SQL databases to high-density time-series data from OT sensors.

  • 02

    Regulatory Compliance Ensuring every data operation is fully auditable and validated according to strict GxP requirements.

  • 03

    Restrictive Environment Building high-performance infrastructure in isolated on-premise networks requiring precise offline dependency management.

  • 04

    Validation Continuity Creating an environment that ensures Data Integrity at every stage – from ingestion to final reporting.

Our solution

We implemented an agile Ontology-Driven Data Lakehouse architecture. This approach separates business logic from code, allowing the system to "understand" physical connections. The solution entailed:

  1. Semantic Process Model

    Using ontologies to map physical production parameters to universal business classes for rapid onboarding.

  2. Streaming Readiness

    A hybrid architecture designed for both batch processing and continuous real-time data ingestion.

  3. Modular Transforms

    Pre-validated processing components that automatically align source data with the target business model.

  4. Rapid Deployment

    Full containerization allowing the complete deployment of platform components onto infrastructure in just 4 hours.

Technology used

  • Protégé (Ontology)
  • Apache Spark
  • Kedro (Pipelines)
  • Apache Airflow
  • Delta Lake
  • Kubernetes (k3s)

The outcome

The project delivered a validated data engine that transforms raw process information into business value. It enables a unique correlation between R&D parameters and mass production, serving as the foundation for the client's upcoming Digital Twin project.

  • Efficiency Gains
  • Eliminated Silos
  • On-Demand Scalability
  • Full Compliance

What we implemented

  • Logic Separation Business rules reside in the Ontology, not the code, allowing experts to manage the model effortlessly.
  • Automated Lifecycle The system automatically recognizes relationships between objects (e.g., LIMS Samples vs SCADA Batches).
  • Advanced Analytics Moving beyond "raw joins" to deliver data in full business context, enabling future Digital Twin capabilities.
  • Hybrid Handling A unified model capable of handling both batch-oriented data and real-time streaming simultaneously.
Patryk Konarski
In classic ETL, business logic is deeply embedded within the code. In our solution, the logic resides in the Ontology. This empowers non-technical Domain Experts to influence the data model without writing code. Unlike traditional approaches that provide only "raw joins," this system "understands" the physical connections within the biotechnological process. This delivers data in its full business context, creating the essential foundation for Advanced Analytics and Digital Twins.
Patryk Konarski — Data Platform Leader

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