How Real-Time Data Middle Platforms are Transforming the Telecom Industry

In the era of digital, intelligent, and data‑driven transformation, data has become a critical production factor, especially for telecom operators facing explosive growth in 5G‑driven traffic and increasingly homogeneous services. Traditional offline data warehouses can no longer meet the demand for rapid, real‑time insights, prompting a shift toward a unified data middle platform.

Necessity of a Real‑Time Data Middle Platform – Most enterprises still build real‑time applications in a siloed, "chimney" fashion, where each new use case requires a separate stream‑processing job, leading to data islands and difficulty adapting to fast‑changing front‑end requirements. A real‑time data middle platform connects data producers and consumers through integrated, scenario‑driven, and real‑time capabilities, turning raw data into reusable assets and business opportunities.

Design of the Telecom Real‑Time Data Middle Platform – The platform adopts a five‑layer architecture:

Real‑time Ingestion Layer : Collects CDC‑formatted data from B‑domain Kafka and message‑type data from O‑domain.

Compute Framework Layer : Uses the open‑source Flink engine for real‑time computation.

Real‑time Model Layer : Constructs multi‑dimensional models (dynamic, event, and time‑series) that abstract data characteristics for upstream consumption.

Real‑time Service Layer : Provides unified, end‑to‑end data services for various scenarios such as location‑based services, precise marketing, and passenger‑flow analysis.

Governance Layer : Implements unified development, pattern design, and operational support tailored to real‑time data characteristics.

The platform also inherits the classic data‑warehouse layering (ODS, DWD, DWS, ADS) and introduces a streaming model layer and a compute model layer to avoid redundant "chimney" constructions. The streaming model layer standardizes and cleans raw streams, storing them in Kafka, while the compute model layer manages registration, auditing, and versioning of dynamic, event, and time‑series models.

Evolution Toward Unified Batch‑Stream (Lakehouse) Architecture – Influenced by the Kappa architecture advocated by Kafka’s founder, the industry is moving away from the heavyweight Lambda approach toward a unified batch‑stream architecture. By leveraging table‑format storage that supports ACID transactions, the platform can seamlessly connect compute engines with storage layers, enabling both batch and real‑time workloads on the same data foundation.

Future Trends and Technical Directions – As lakehouse technologies mature, real‑time middle platforms will increasingly adopt table‑format data organization, batch‑stream integration, and cloud‑native deployment. This evolution reduces latency, simplifies data governance, and supports a broader range of real‑time applications across marketing, operations, and management.

Conclusion – Real‑time data middle platforms empower telecom operators to deliver faster insights, break down data silos, and create a competitive edge. By aligning technology with business needs, they enable agile, scenario‑centric services while lowering development and operational costs, making them indispensable partners in the digital transformation of the telecom industry.