Stream-Batch Unification

Stream-Batch Unified Architecture processes both streaming and batch data within a single system by treating batch as bounded data streams and streaming as unbounded ones. This unified logic enables real-time analysis alongside large-scale batch processing, catering to diverse business needs.

Stream-Batch Unified Architecture processes both streaming and batch data within a single system to meet diverse business needs.

Industry Pain Points

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High Operational Maintenance Costs

Enterprises face high operational costs from maintaining both offline and real-time systems simultaneously.

High Development Costs

Enterprises need to develop two sets of code for offline and real-time processing, resulting in high learning and development costs.

High Data Management Costs

The computational engines for offline and real-time processing are different, making data consistency difficult to guarantee. Furthermore, the use of different storage formats complicates data management.

Value Proposition

Achieve Real-Time Data Processing

Through the real-time computing Stream-Batch Unification architecture, vast amounts of real-time generated data can be processed and analyzed immediately, meeting requirements for data timeliness and accuracy.

Support Large-Scale Batch Processing

Under the real-time computing Stream-Batch Unification architecture, large-scale batch data can be processed and computed efficiently, fulfilling tasks that require offline analysis.

Unified Data Processing Platform

The real-time computing Stream-Batch Unification architecture can unify the data processing platform, simplify the system architecture, reduce operational costs, and improve the efficiency of the technical team.

Achieve Real-Time Data Processing

Through the real-time computing Stream-Batch Unification architecture, vast amounts of real-time generated data can be processed and analyzed immediately, meeting requirements for data timeliness and accuracy.

Support Large-Scale Batch Processing

Under the real-time computing Stream-Batch Unification architecture, large-scale batch data can be processed and computed efficiently, fulfilling tasks that require offline analysis.

Unified Data Processing Platform

The real-time computing Stream-Batch Unification architecture can unify the data processing platform, simplify the system architecture, reduce operational costs, and improve the efficiency of the technical team.

Solution Architecture

Customer Cases

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Unified Stream-Batch Platform for a Provincial Medical Insurance Administration

A provincial medical insurance bureau required aggregation of data from all prefecture-level cities to its provincial platform for data governance. After Phase 1 implementation, unavoidable data rollbacks from multiple technical issues resulted in substantial dirty data at the provincial level, operational challenges for cities, and complex data pipelines hindering rapid response. This phase delivers a stable, unified stream-batch data pipeline, centrally manages synchronization across 14 cities, upgrades data synchronization tools, alleviates municipal operational burdens, and ensures complete data collection with integrity verification from all 14 cities before 01:00 the following day.

A provincial medical insurance bureau required aggregation of data from all prefecture-level cities to its provincial platform for data governance. After Phase 1 implementation, unavoidable data rollbacks from multiple technical issues resulted in substantial dirty data at the provincial level, operational challenges for cities, and complex data pipelines hindering rapid response. This phase delivers a stable, unified stream-batch data pipeline, centrally manages synchronization across 14 cities, upgrades data synchronization tools, alleviates municipal operational burdens, and ensures complete data collection with integrity verification from all 14 cities before 01:00 the following day.