Delta Layout and Quality Gates

Good Delta layout reduces unnecessary scans while quality gates reduce downstream surprises. The goal is not to optimize every table. The goal is to optimize the tables that sit on critical paths.

Practical quality checks

• Assert required fields before promotion from bronze to silver.
• Track null rates for features consumed by models.
• Use table history when a consumer reports a regression.
• Review file counts and average file size when query latency changes.

Tiny monitoring sketch

from pyspark.sql import functions as F

profile = (
    spark.table("main.silver.events")
    .groupBy("event_date")
    .agg(
        F.count("*").alias("rows"),
        F.sum(F.col("user_id").isNull().cast("int")).alias("missing_users"),
    )
)

display(profile.orderBy(F.desc("event_date")))

Design checkpoint

A table is ready for broad reuse when quality checks, layout expectations, and ownership are all visible in the same review.