ASAP: Reimagining the Data Lifecycle using Application Semantic-Aware Processing

2026-08-12

AI Summary

This work addresses the longstanding challenge in large-scale data processing pipelines of simultaneously achieving low cost, high performance, and strong scalability. The authors propose ASAP (Application Semantic-Aware Processing), a novel paradigm that, for the first time, treats semantic preservation as a first-class design principle across all stages of the data lifecycle. ASAP unifies semantic-aware processing primitives in data ingestion, transmission, storage, and analytics. By integrating techniques such as approximate query processing (AQP), sketching, compression, and aggregation, ASAP enables cross-stage co-optimization and cross-domain technique reuse. Experimental evaluation demonstrates that ASAP significantly improves the cost–speed–scalability (CSP) trade-off across multiple real-world applications, yielding performance gains of up to three orders of magnitude.

Abstract

Across many domains (e.g., observability, networking, security), data processing pipelines face what we refer to as the CSP problem: achieving low Cost at large Scale, while maintaining high Performance. In response, we see several efforts to tackle CSP in various stages of the Collect-Transmit-Store-Analyze data lifecycle; such as approximate query processing in databases or sketches in network routers. Our work is driven by the simple insight: "seeing the forest for the trees". These proposed solutions (e.g., AQP, sketching, compression, rollups) addressing CSP share a common property - they exploit semantic-preserving opportunities to support application needs. In this paper, we make a case for ASAP, a paradigm that makes Application Semantic-Aware Processing (ASAP) a first-class design principle in data processing pipelines. We argue that by taking a unified view across ASAP primitives developed in different domains, across the entire data lifecycle, we can unlock new opportunities to tackle the CSP problem. In particular, we can: (i) enable novel cross-lifecycle optimizations such as analytics run directly on sketches computed at the source; (ii) leverage primitives developed in other application domains; and (iii) enable widespread adoption of these powerful techniques. We discuss research challenges in socializing the benefits of the ASAP paradigm, and show preliminary evidence that adopting ASAP can yield up to 3 orders of magnitude improvements in the CSP tradeoff for many application domains.

Problem

• CSP problem
• data lifecycle
• semantic-aware processing
• cost-performance tradeoff
• large-scale data processing

Innovation

• Application Semantic-Aware Processing
• Data Lifecycle Optimization
• CSP Tradeoff
• Cross-domain Primitives
• Sketch-based Analytics