It's remarkable how little has changed in data engineering over the past 6.5 years. I wrote an article back then titled "Data Engineering, the Future of Data Warehousing" and revisiting it now is eye-opening.

The core of data engineering remains largely the same:

- Python is still the dominant language

- Key challenges persist: data integration, pipeline maintenance, quality assurance, process automation, and big data handling

- Fundamental skills (SQL, data modeling, ETL) are as crucial as ever

Some predictions that held true:

1. Python's rise in data engineering and science

2. Shift towards more programmatic skills in data roles

3. Growing importance of data engineering in data-driven companies

While tools have evolved, the underlying techniques and craft haven't changed dramatically. We've seen the rise of ELT and more flexible architectures, but the core purpose remains: making data accessible, reliable, and valuable for businesses. What's your take?