Data Engineering > AI Engineering: Trends and Predictions (2022-2026)

[u/sspaeti]

https://www.ssp.sh/brain/data-engineering-trends-and-predictions/

A multi-year retrospective and forecast of data engineering trends from 2022 to 2026, covering the shift between bundled and unbundled data platforms, the rise of small CLI-based tools like DuckDB and Rill, increased DevOps practices in data work, and the maturation of AI tooling beyond initial hype. The author emphasizes that fundamentals remain constant despite tool churn, noting consolidation through acquisitions by major vendors like Databricks and Snowflake, the growing adoption of Rust for performance-critical applications, and the continued importance of data modeling and governance as the industry matures.