Marlin Data Fabric (MDF)

Automated Network Discovery & Data Extraction Engine

Scope

Marlin Data Fabric (MDF) is an end-to-end data framework designed to seamlessly orchestrate the entire data lifecycle—from ingestion and migration to transformation, quality assurance, and AI-driven intelligence. Built for modern data ecosystems, Marlin Data Fabric provides a unified layer that connects disparate data sources, automates complex workflows, and ensures data is trusted, clean, and ready for advanced analytics and machine learning.

 

 

Default datasets — out of the box

MNI ships with ready-made geographic datasets and dashboards, so the business sees the network the moment it's connected:

Unified Data Integration Layer
  • Integration of multiple data sources (OSS/BSS, GIS, NMS, field data)
  • API‑driven data ingestion and exposure
  • Data normalization across heterogeneous systems
  • Support for real‑time and batch data flows
Data Governance and Integrity
  • Single source of truth across PNI / Digital Twin
  • Versioning and lifecycle management
  • Data validation and consistency rules
  • Conflict resolution and master data ownership
Scalable Data Architecture
  • Distributed and scalable architecture
  • Support for large‑scale network datasets
  • Performance optimization and efficient data access
  • Alignment with cloud / hybrid deployment models
Data Access and Exposure Framework
  • Secure API framework for data access
  • Integration with reporting, analytics, and MNI
  • Role‑based access to datasets
  • Enablement of downstream systems and partners

How good is your data?

Under the hood: AI/ML and computer vision read unstructured legacy — spreadsheets, PDFs, Visio, diagrams — alongside geospatial tooling, always with a human in the loop. Start with a Data Assessment, or see how we use AI.

Ask for a Data AssessmentHow we use AI?

Book a demo

A focused demo, mapped to your network and your processes — run by someone who has migrated networks like yours. Or start with the question that changes everything: how good is your data, really?