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Intermediate. Assumes working knowledge.

752 views1 year ago1 recommendation

Databases and dataAI and machine learningData engineering and warehousesTransformers and LLMsThe tech industry

A walkthrough of the Metabase Community Data Stack Report 2025, analyzing tool adoption and the reality of AI in business intelligence.

In this session, we walked through the top patterns from the Metabase Community Data Stack Report 2025, showed how we approached the analysis, and shared our take on why certain tools (and workarounds) are winning. If you're building, maintaining, or rethinking a stack, this is a technical look at what real teams are doing, not just what vendors say they are. Check out the full report: metaba.se/data-stack-report-2025 Learn more about Metabase: 🔷 Website: www.metabase.com/learn/cheat-sheets/which-chart-to-use?utm_source=youtube&utm_medium=social&utm_campaign=chart-cheat-sheet 🔷 Get started: store.metabase.com/checkout?utm_source=youtube&utm_medium=so… 🔷 Docs: metabase.com/docs/latest/?utm_source=youtube&utm_medium=soci…

Why it's here

1 person recommends this

What people say. Shared to highlight the gap between AI promises and the reality of non-technical user adoption in data teams.

u/Miserable_Fold4086in r/dataengineering • Recommends

The AI promise vs reality: 45% of teams have zero non-technical user adoption Sharing a clip from the recent Data Stack Report webinar. Key stat: 45% of surveyed orgs have zero non-technical AI adoption for data work. The promise was that AI would eliminate the need for SQL skills and make data accessible to everyone. Reality check: business users still aren't self-serving their data needs, even with AI "superpowers." Maybe the barrier was never technical complexity. Maybe it's trust, workflow integration, or just that people prefer asking humans for answers. Thoughts? Is this matching what you're seeing? \--> full report
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