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Microsoft’s latest database is a fast, scalable PostgreSQL for cloud-native and AI developers.
Where Microsoft promises enterprises better understanding of their data for workers and autonomous agents alike, analysts fear deployment hurdles and vendor lock-in.
Third time’s the charm? Microsoft hopes the scalability of Azure HorizonDB, will lure new customers where its two existing PostgreSQL databases did not.
Split your metadata from your files, and suddenly your sluggish document system becomes fast, scalable and surprisingly cheap to run.
The new capability supports tables, figures, and diagrams with spatial metadata, making documents searchable and actionable in AI workflows.
With agentic AI, the database must evolve from a passive ledger to an active reasoning engine that informs, guides, and enables autonomous action.
You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs.
Google’s new tool abstracts SQL into a visual interface, helping enterprises manage cloud workload insights without deep technical expertise.
The data mesh architecture promised to eliminate the inefficiencies and errors of the data lake. What went wrong?