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Data and analytics

Data engineering in the model era: preparing data, analytics without SQL, and why text-to-SQL keeps breaking.

3 articles

IBM puts Granite time-series models inside Confluent's Flink SQL

IBM has moved its Granite time-series foundation models inside Confluent's stream processor. Four of them — PatchTST-FM, FlowState, TTM and TSPulse — are in early access on Confluent Cloud on AWS, callable from Flink SQL through two functions Confluent already ships, AI_FORECAST and AI_DETECT_ANOMALIES. Swapping one model for another is a single parameter change, with no pipeline to rebuild.…

Amazon's seller analytics agent skips SQL and answers in under 15 seconds

An e-commerce seller makes decisions on the fly all day: what to push in ads, where sales slipped, which products drag the business down and which drive growth. There is no shortage of data, but the value in it is rarely obvious. Getting an answer means opening several tools, knowing which report holds the number, how to build it, which filters to set — and then reading the result correctly.…

A 31-subtype error taxonomy beats blind retries in text-to-SQL

Turning a human question into correct SQL is a surprisingly hard problem. Large language models write valid syntax well but miss the logic easily: they confuse tables, join on the wrong key, forget GROUP BY, apply the wrong filters. Plain self-correction from execution results doesn't always help — a query can run fine and even return something plausible that still isn't what the user asked…