Survey Reminder: 2 Minutes, Big Impact

2025-11-05 · Source: Practical Data Modeling · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Fundamental Awareness, quick

Summary

The 2026 Practical Data State of Data Engineering survey is currently open, seeking input from data professionals across various roles including data engineering, analytics, AI/ML, platform, and architecture. With nearly 600 responses already collected, the survey aims to gather ground-truth insights into how data teams operate in 2025, contrasting practitioner experiences with vendor claims or analyst firm suggestions. The survey takes approximately 2-3 minutes to complete and will close on January 10, 2026. A comprehensive report based on the collected data will be released afterward and made freely available to all participants and interested parties.

Key takeaway

For any data professional involved in data engineering, analytics, AI/ML, platform, or architecture, your 2-3 minute contribution to the 2026 Practical Data State of Data Engineering survey is crucial. By sharing your perspective before January 10, 2026, you directly help shape a report that reflects real-world data team operations, providing valuable, unbiased insights for the entire community.

Key insights

The 2026 Practical Data State of Data Engineering survey seeks ground-truth insights from data practitioners.

Principles

• Practitioner input defines actual data team operations.
• Broader participation enhances report utility.

Method

A short, 2-3 minute survey collects data on how data teams function, aiming for a practitioner-driven perspective.

In practice

• Participate in the 2026 State of Data Engineering survey.
• Access the free report after January 10, 2026.

Topics

• Data Engineering
• Data Analytics
• AI/ML
• Data Architecture
• Industry Surveys

Best for: Data Engineer, Data Analyst, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Practical Data Modeling.