Summary

Reis presents the results of a 1,101-response data engineering community survey, delivered as an interactive explorer rather than a traditional PDF report. Key findings reveal that while AI tool usage is nearly universal among individual practitioners, organizational AI adoption remains largely experimental, and data modeling continues to be a major pain point driven by organizational rather than technical challenges.

Key Insight

AI has become ubiquitous among individual data engineers but organizational adoption is stalled, and the field's biggest obstacles remain people problems — poor leadership direction, unclear requirements, and pressure to skip modeling — not technology.

Spicy Quotes (click to share)

• 3

  Rather than bury the results in a 30-page PDF behind a email form, I built something different: an interactive explorer where you can query the data yourself.

• 5

  AI is table stakes. 82% of you use AI tools daily or more. Only 3.7% find them unhelpful. But organizational adoption lags way behind.

• 5

  The bottlenecks aren't technical. Legacy systems top the list (25%), but lack of leadership direction (21%) and poor requirements (19%) are close behind. People problems rival tech debt.

• 4

  Go find something I missed.

Tone

analytical