Overview

What is the Digital Data Backbone?

The Digital Data Backbone (DDB) is a comprehensive Python-based framework for streaming data from various sources to the MFI Digital Data Backbone. It provides tools for data ingestion, metadata storage, and retrieval services — enabling seamless ingestion of real-time data from diverse sources including IoT sensors, file systems, and MQTT brokers into a central pub-sub messaging system.

Core Capabilities

Connect: Equipment and sensors to the digital backbone through communication protocols and interfaces.
Collect: Time series data, video files, photographs, images and sketches from manufacturing equipment.
Contextualize: Data through user entered data descriptions (metadata) to ensure integrity and usefulness.

Architecture at a Glance

The DDB follows an event-driven architecture using MQTT as the central pub-sub broker.

ddb-layers

Data flows from Data Adapters → MQTT Broker → Database Nodes. Each component is independently configurable and pluggable.

Key Components

Component	Description	Repository
Core Library	Python package providing data adapters, streamers, and topic families	`mfi_ddb_package`
Data Adapter App	REST API web application for managing data adapters on edge devices	`mfi_ddb_data_adapter`
Database Nodes	Compatible database storage implementations	`mfi_ddb_database_nodes`
Retrieval API	Metadata store and REST API for data queries	`mfi_ddb_retrieval_api`

Who Is This For?

Researchers who need to collect and analyze manufacturing data
Engineers building data collection infrastructure for production environments
Integrators connecting existing equipment to digital systems
Operators managing dashboards and monitoring production metrics