Without clean data, every AI model is only as good as the garbage you feed it. Data engineering ensures that data is reliably collected, transformed, and delivered.
Modern data stacks with tools like dbt, Apache Airflow, and Snowflake have significantly lowered the barrier to entry. Still, the discipline remains demanding and requires a solid understanding of data modeling.
Investing in AI without taking data engineering seriously is building on sand. Data quality determines the success or failure of every ML project.
