query optimization
3 stories filed under query optimization on Beyond Market Intelligence. The newest of them: “File Compaction's Real Impact on Query Speed, Tested Across Three SQL Workloads”, “QueryStory secures $6M to make AI queries coherent and trustworthy”, and “Scale Your AI Agents with Postgres's Native Multi-Modal Power”. Compacting 1,000 Apache Iceberg files into just six sounds drastic, but this benchmark shows why fewer, larger files can dramatically accelerate query speed. QueryStory is betting that the gap between what an AI says and what it means can be closed with the right guardrails. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every query optimization story on Beyond Market Intelligence, newest first.
File Compaction's Real Impact on Query Speed, Tested Across Three SQL Workloads
Compacting 1,000 Apache Iceberg files into just six sounds drastic, but this benchmark shows why fewer, larger files can dramatically accelerate query speed. Testing across three SQL workloads, the results are clear: file count matters more than most users realize. We appreciate how this experiment grounds the advice in real performance data rather than theory. It's a practical reminder that storage hygiene directly shapes analytics speed.
QueryStory secures $6M to make AI queries coherent and trustworthy
QueryStory is betting that the gap between what an AI says and what it means can be closed with the right guardrails. The startup emerged from stealth with $6 million in seed funding, pairing LLMs with cybersecurity expertise to make AI queries more coherent. That focus on trust feels timely, especially if you have been following our piece on questioning AI clones. We are watching closely to see if their approach turns skepticism into a feature, not a flaw.
Scale Your AI Agents with Postgres's Native Multi-Modal Power
Postgres has quietly become the backbone for production AI, and Gwen Shapira's session makes a compelling case for why that matters. She shows how teams are moving beyond toy demos, using Postgres's JSONB parsing and high-recall HNSW indexing to ground LLMs in both deterministic and semantic context. The 4x speedup from vector quantization is a practical win, but the real insight is managing agentic memory without losing reliability. For those exploring distributed training challenges, our guide on distributed algorithms pairs well with this foundation.