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Ask any data architect what seemed impossible to cost-effectively achieve just five years ago, and you’ll hear about real-time fraud detection systems processing millions of transactions per second, AI-powered search engines that understand context across petabytes of unstructured data and distributed analytics platforms that respect data sovereignty while enabling global insights.
These aren’t aspirational use cases anymore, but production workloads running — and running well — on 100% open source data infrastructure.
The difference…
