Every year there’s a new framework, a new “no-code” tool, or a new data platform that promises to replace SQL. And yet—SQL remains the most universal, reliable, and widely adopted interface for working with data.

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?