Data Lakehouse cookbook

Every Iceberg tutorial assumes you already have a Spark cluster running. What if you just want to peek at one table?

A bad UPDATE just overwrote a table, and everyone downstream is already looking at the wrong numbers.

Analytics wants to query the new table in Trino, the pipeline team already writes it with Spark, and both need to see the same data.

Every update to your lakehouse table rewrites whole files, and the write job is starting to crawl.

Every snapshot from every write is still sitting in storage. Nobody ever told it to stop.

Inserts land fine, but every update or delete Debezium sends through shows up as a brand new row instead.

The table isn't getting any bigger, but the same query takes longer every single week.

A column's type needed to change, so someone dropped it and added it back, and now a trusted count is wrong.

The table has years of Parquet files in it, and a full rewrite would tie up the cluster all weekend.

A streaming job that ran fine for months just started failing every few minutes, and nobody touched the code.

Finance needs the full customer table, and the support team down the hall queries the exact same table but should never see a salary column.