Data Engineering: Utter Bullshit (and Why Solid Fundamentals Still Matter More Than Any Framework
In an age overflowing with buzzwords and flashy slide decks, it’s easy to believe that
Data Engineering: Utter Bullshit (and Why Solid Fundamentals Still Matter More Than Any Framework Name)
In an age overflowing with buzzwords and flashy slide decks, it’s easy to believe that
- Data Fabric magically stitches every system together,
- Zero ETL means you’ll never write another pipeline, and
- the Modern Data Stack™ is a one-size-fits-all solution for every organization.
The reality? Data engineering still demands rolling up your sleeves. You still have to clean and transform data, wrestle with schema drift and late-arriving events, orchestrate DAGs, monitor jobs, tune performance, and answer business questions that seem to change every week.
Data Fabric – essentially metadata + virtualization + sync. Sounds great, but you still need to configure connectors, enforce security, and handle dirty data yourself.
Zero ETL – marketed as “just query data in place,” yet the data still must be normalized, aggregated, and deduplicated before it’s useful.
Medallion Architecture (Bronze → Silver → Gold) – really just classic data warehousing with a new label; inter-layer dependencies still create headaches.
Modern Data Stack™ (Snowflake + dbt + Looker) – powerful, but for a five-person team with 10 GB of data it may be costly overkill.
What data engineers should actually focus on
- Designing pipelines that are maintainable and observable
- Understanding the business use case behind every dataset
- Balancing cost and performance instead of chasing hype
- Picking tools because they solve your problem, not because they’re trending
- Communicating clearly in an increasingly complex landscape
We’re not anti-tool—just realistic: tools aren’t magic. Frameworks evolve, but sound engineering fundamentals never go out of style.
Inspired by Kirill Bobrov’s article “Data Engineering: Now with 30% More Bullshit.”