A few reasons it still wins:
It’s declarative. You describe what you want, not how to compute it.
It’s stable. Decades of optimizations, battle-tested across industries.
It’s portable. From Postgres to BigQuery to Snowflake to DuckDB, SQL is everywhere.
It’s optimized. Modern engines can handle insane workloads with minimal tuning.
It’s the one language every analyst, engineer, and ML person can agree on.
Even modern “data engineering” tools—dbt, Spark SQL, Trino, BigQuery ML—are simply bringing SQL to new environments, not replacing it.
The ecosystem keeps changing, but SQL’s simplicity and longevity make it the closest thing we have to a universal data language.
Curious to hear how others feel: Is SQL’s dominance a sign of maturity, or is something genuinely better on the horizon?
willvarfar•13m ago
And the dialects of the language itself, SQL keeps getting more relaxed and interoperable and forgiving. With WITH and CTEs and things it keeps getting easier and cleaner to express things, so it's going steadily in the right direction. There are still a few slight differences in the syntax for window functions between bigquery and duckdb, for an example I fight often, but they are all a lot closer to each other today than they used to be back 30 years ago when you had to use SQL differently and construct complicated queries differently just to run on Oracle vs MySQL vs Postgres.