What Is a Data Middle Platform and How Does It Transform Enterprise Data Management?

Data Middle Platform Overview

A data middle platform refers to a set of technologies that collect, compute, store, and process massive amounts of data while unifying standards and definitions, turning raw data into standardized assets that can be efficiently served to customers.

Design Principles

Data consistency and standardization

Data usability and service orientation

Data independence and scalability

Data security

Hierarchical data management mechanisms

Core Functions

Overall Architecture

The platform consists of a functional architecture, a layered architecture, a logical architecture, and a data architecture.

Functional Architecture

Key subsystems include a unified data collection and access platform, a centralized processing platform, an organization management platform, a global governance platform, a data sharing platform, an analytics and mining platform, a knowledge graph platform, a unified management platform, and a visualization platform.

Layered Architecture

Based on business needs and vision, the platform integrates a data acquisition perception system, a data resource integration system, and an information sharing service system, embedding security and standards throughout to continuously enhance capabilities for data ingestion, processing, organization, mining, governance, and service.

Logical Architecture

The platform supports standardized data ingestion, modular processing, fine‑grained organization, full‑dimensional fusion, precise sharing, and secure integration, forming three major systems: big‑data perception, data resource integration, and data sharing services.

Data Architecture

For heterogeneous multi‑source scenarios, the data architecture provides stable, efficient support for data access, fusion, and intelligent applications, covering raw, resource, topic, and business libraries, as well as a knowledge base, index library, metadata repository, and experiment space.

Data Unified Collection and Access Platform

Platform Architecture

The platform adopts a unified data access model, providing modular, standardized ingestion for multi‑source heterogeneous data, supporting comprehensive collection, dynamic configuration, task scheduling, encryption, and breakpoint‑resume capabilities while maintaining data catalogs and lineage.

Data Flow

The platform offers one‑stop migration, preliminary cleaning, visual task scheduling, and supports downstream analytics and governance.

Platform Functions

Data ingestion supporting relational databases, NoSQL, distributed storage, streaming systems, message middleware, and file systems.

Configurable ingestion strategies for structured, semi‑structured, and unstructured data, with plugin‑based parsers and adapters.

Breakpoint‑resume for reliable transmission and recovery.

Task management with monitoring, exception handling, and rescheduling.

Data cleaning to filter non‑conforming records.

Statistical reporting on ingestion volume, file counts, latency, sources, and destinations.

Data reconciliation to verify completeness and consistency.

Quality assessment and reporting.

Data Centralized Processing Platform

Processing standardizes data through extraction, cleaning, linking, identification, and objectification, supporting both real‑time and batch computation, and incorporates AI techniques such as graph computation, in‑memory processing, model inference, and knowledge graph construction.

Platform Architecture

The open architecture enables dynamic workflow orchestration and integrates NLP, multimedia processing, and machine learning for intelligent perception.

Data Flow

Key functions include data extraction (producing EXF files), cleaning, linking heterogeneous sources, comparison (structured and unstructured), identification, error correction, and task scheduling with fault‑tolerant, breakpoint‑resume capabilities.

Data Organization Management Platform

Platform Architecture

The platform manages data through layered libraries: raw library (stores original formats), resource library (standardized public datasets), topic library (customized per business domain), and business library (supports specific operational needs). A knowledge library aggregates domain expertise and rules.

Data Global Governance Platform

Platform Architecture

Governance covers standard management, metadata management, asset management, quality control, operations monitoring, and lifecycle management, ensuring data security, classification, and access control throughout the data lifecycle.

Data Flow

Metadata and lineage management enable traceability, while quality management provides monitoring, analysis, and continuous improvement.

Data Quality Management Platform

Standard management enforces uniform data definitions; lifecycle management spans acquisition, storage, integration, analysis, presentation, archiving, and destruction. Quality metrics quantify data health, guiding cleaning, integration, and value extraction.

Data Sharing Service Platform

Platform Architecture

The service bus exposes APIs for data services, supporting registration, approval, publishing, discovery, and secure access. It enables subscription, push, download, and exchange services across nodes.

Data Flow

Use cases include service subscription, data push, and download, with authentication and cross‑domain capabilities.

Data Visualization Platform

Platform Architecture

The platform offers over 50 visualization components, including charts, maps, dashboards, and 3D graphics, supporting ad‑hoc queries, data insights, mobile reports, large‑screen displays, and interactive analysis.

Data Flow

Features include flexible layout, what‑if analysis, drill‑down, multi‑dimensional queries, 2D/3D linkage, historical playback, and report generation.

Source: Data Academy