Summary

The article discusses the limitations of traditional ETL processes in addressing the meaning of data, emphasizing the need for a Context Store to provide semantic clarity. It introduces the ECL framework (Extract, Contextualize, Link) as a new approach to data engineering that prioritizes meaning over mere data movement. The author highlights the accountability gap in transformation logic and the erosion of context through data pipelines. The piece advocates for recognizing existing infrastructure patterns to build a Context Store that meets the needs of modern autonomous agents.

Data Problems Addressed