Snowflake vs Databricks in 2026: Pick the Workload, Not the Logo
An honest head-to-head: SQL warehousing vs Spark lakehouse, Iceberg interoperability, Cortex vs Mosaic, and when you actually need both.
- snowflake
- databricks
- tools
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An honest head-to-head: SQL warehousing vs Spark lakehouse, Iceberg interoperability, Cortex vs Mosaic, and when you actually need both.
Table-format choice in 2026: deletion vectors, catalogs, and which engine you are willing to lock in.
What the lakehouse actually is, how Delta Lake adds ACID transactions to cheap object storage, and the medallion architecture in practice.
What DLT genuinely buys you, how expectations and streaming tables behave in production, and the four situations where I still write plain Spark jobs instead.
How I take 30 percent off a Databricks bill in a week: kill all-purpose clusters for jobs, price spot properly, test Photon per workload, and rank spend with system.billing.
How I structure Unity Catalog for real teams: three-level namespace design, group-based grants, lineage that works, and a hive_metastore migration that ships.
How the _delta_log actually works: JSON commits, optimistic concurrency, checkpoints, deletion vectors, and the small-file problem nobody escapes.