After three years of wrestling with chimney-style architectures, many engineering teams are discovering that traditional monolithic storage no longer fits modern data workloads. This post captures that shift, explaining how vector databases handle similarity search and AI embeddings efficiently, while fused storage unifies transactional and analytical processing. The author shares concrete pain points—like data duplication, slow queries, and high maintenance overhead—that pushed them to explore these alternatives. For backend engineers and architects, the takeaway is clear: specialized storage engines are becoming essential as AI and real-time analytics demand more from infrastructure. The article also touches on practical migration considerations, such as data consistency and cost trade-offs. While not a deep technical tutorial, it offers a valuable perspective on why the storage landscape is evolving and what teams should evaluate before committing to a new stack.
A practical look at why teams are moving from siloed storage to vector databases and fused storage, based on real migration pain.