Why ad platforms run six databases to answer one question

Ad platforms accumulate six or more databases not by design but through incremental decisions: a key-value store for auction lookups, a columnar store for real-time aggregates, a search index for audience filtering, caches, and eventually a vector store. Each addition is locally rational but collectively creates a lambda-style architecture with duplicate data, pipeline latency of 40+ minutes, reconciliation overhead, and fragmented ownership. The core problem is that warehouses and lakehouses handle batch OLAP well but cannot serve the low-latency, high-concurrency HTAP workloads ad platforms require. HTAP engines that store data in both row and columnar formats can ingest streams and make them immediately queryable, eliminating the need for most secondary systems. The recommended approach is incremental migration starting with the highest-friction workloads, while keeping the warehouse for historical archives and model training.

Nguồn: https://www.singlestore.com/blog/adtech-serving-layer-sprawl. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.

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Why fixing your data architecture matters more than upgrading your detection models

AI-driven security tools underperform not because of weak models, but because of poor data architecture upstream. Enterprises running dozens of security products accumulate fragmented telemetry with inconsistent schemas, causing schema drift that silently degrades ML detection quality over time. Stale behavioral baselines compound the problem as hybrid work and cloud adoption change user patterns faster than models can adapt. The root cause is an organizational gap: no one owns the analytical consistency of security data flowing between engineering and SOC teams. Three actionable priorities are proposed: standardize telemetry schemas across the security stack, build data quality monitoring into every ingestion pipeline, and apply data governance discipline to security telemetry the same way it's applied to financial data.

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