Reusable Streaming Pipeline Frameworks for Enterprise Lakehouse Analytics

Abstract

Operational intelligence, predictive analytics and automatic decision systems rely on real-time streams of data to be increasingly utilized by modern enterprises. Lakehouse architectures are valuable because they consolidate data lakes, warehouses, however, no reusable, standardized, and re-engineered streaming pipeline designs exist meaning fragmented deployment, overlapping logic, operation instability, and scale deficits. This paper represents a proposed system of reusable streaming pipelines in enterprise lakehouse analytics that emphasizes the concepts of modularity and portability, as well as outlining a design-centric governance method. Its architecture breaks down streaming ingestion, transformation, quality enforcement, enrichment and storage functions into loosely coupled, interoperable layers which can be dynamically implemented in analytical application cases. The domains adaptive process enforced by schema-conscious processing, event-driven connectors, metadata-oriented orchestration, and policy-driven quality controls respectively enable the domain consistent behavior and domains adaptation respectively. To test scalability and reusability, a multi-tenant reference implementation was run on a cloud-native lakehouse stack on distributed stream processing engines and message brokers. Empirical analysis has shown that it reduces the development cycle time by a significant margin, enhances the reliability of pipeline, and reliability in throughput in comparison with monolithic pipeline implementation. Findings suggest a maximum 42 percent reduction in the development effort in pipelines, 35 percent in the development of ingestion latency, and a better recovery of faults with changing loads.

The results indicate that structured reusability is a performance-facilitating architecture choice of enterprise lakehouse ecosystems, rather than just an architectural preference. The given approach provides a long-term basis of continuous intelligence, mass operational observation, and future AI-driven automation efforts because it allows generating repeatable, managed, and analytics-friendly streaming pipelines.