Databricks

From zero to a running job on a Causeway Databricks workspace. Twenty minutes, no notebooks required.

Three physically distinct compute offerings, one right default per workload class, and the decision framework for getting it right the first time.

Three products consolidated into one data-engineering plane. Knowing which piece does what prevents architectural flailing.

The object model that governs every table, view, volume, and function on a Causeway Databricks workspace. What lives where, who can touch what, and why lineage comes for free.

YAML in, deployed resources out. The only sanctioned way to ship anything to a Causeway Databricks workspace.

Managed Postgres inside Databricks. When to reach for it, the adoption ladder, and the Postgres fundamentals that managed does not absolve you from.

A walkthrough of building an LDP (formerly Delta Live Tables) from source to silver, with expectations, CDC, and the modes that trip teams up.

Cluster will not start, cluster terminates unexpectedly, cluster runs but queries hang. The 5-minute triage and the next 20.

The Databricks CLI subcommands you actually use day-to-day, in the order you hit them.

Causeway's rules for how a Databricks workload is structured, named, and deployed. Enforced at review time; exceptions require an RFD.

What a Causeway Databricks workload must satisfy before its first prod deploy, and what it must keep satisfying. Enforced at the promote-to-prod gate.

Exhaustive config options for Databricks SQL warehouses: sizing, scaling, auto-stop, Photon, type selection.

Symptom-first lookup for the errors you hit most on a Databricks workspace: cluster launches, UC permissions, Delta writes, init scripts, Lakeflow pipelines.