For two decades, DBAs protected production databases from analytics workloads. AI agents have ended that truce and the consequences go deeper than most realise.
AI agents bypass dashboards and query databases directly. This article explains what that means for the architecture of enterprise data systems and systems of record.
Databases were designed for human-paced interaction – sessions, think time and deliberate intent. AI agents remove all three assumptions simultaneously.
AI inferencing is a database problem disguised as an AI problem. Real-time model workloads expose latency and concurrency demands that enterprise storage wasn’t designed for.
Hyperscalers have solved cloud infrastructure for most workloads. High-performance RDBMS remains the exception – the gap between cloud promises and database reality is still real.
Not all cloud vCPUs are equal. This article explains how constrained and optimised CPU configurations affect enterprise database performance in ways the spec sheet doesn’t reveal.
On-premises databases were overprovisioned for performance. Cloud databases are overprovisioned for a different reason: unpredictable pricing. This article explains the real cost.
Not all database migrations are equal. This article explains the main paths to public cloud and why choosing the wrong one for mission-critical workloads is an expensive mistake.
AWS, Azure and GCP are competing hard for enterprise databases. This article explains why mission-critical RDBMS workloads are the prize and what that means for DBAs.
Now is the winter of our discontent. But rather than dwell on what a terrible year 2020 has been, I thought I'd make my final post of the year something more positive... so I am going to look back on one of the (many) times I made a fool of myself, in the hope that … Continue reading How To Look Stupid (Part #612)
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