Energy AI deployments are stalling not because of model quality but due to two infrastructure problems: physical grid capacity constraints from surging data center power demand, and fragmented data architectures that can't deliver real-time cross-system coordination. Most energy data stacks were designed for human-paced, batch-oriented workflows — but AI-driven demand response, grid operations, and control-room copilots require millisecond-fresh, consistent state across operational, forecasting, and market systems simultaneously. The post argues for HTAP-style operational data systems that unify ingestion, storage, and serving in a single engine, eliminating the multi-hop ETL pipelines that introduce latency and state inconsistency. SingleStore is presented as a concrete solution, with examples of reducing ingestion time from hours to seconds and query latency from minutes to under a second.
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The data is there. The coordination isn’t.What the gap looks like in practice: AMI at scaleDo you have this problem?Why existing architectures hit a ceilingWhat has to changeWhere distributed SQL fits inThe scale of what’s comingSo what do you actually do with this?Sort: