Databricks engineering: lakehouse pipelines on Delta Lake, Spark workloads that stay affordable, and ML workflows that reach production.
Cluster spend without pipeline discipline
Databricks makes powerful things easy to start: notebooks become production jobs, clusters run around the clock for hourly workloads, and nobody remembers why a job exists. The platform rewards engineering discipline: jobs as code, clusters sized per workload, and tables governed in Unity Catalog.
What we build with Databricks
- Lakehouse architectures on Delta Lake with medallion-layer modeling
- Spark pipelines packaged as versioned jobs, not immortal notebooks
- Cluster policies and job compute that match cost to workload
- ML workflows with MLflow tracking from experiment to serving
How we work
Model bronze, silver, gold layers so consumers trust the top
Promote notebooks to tested, versioned jobs before they matter
Use job clusters and autoscaling; always-on is a choice, not a default
Govern tables and access through Unity Catalog from the start
Typical stack
Frequently asked questions
They overlap more each year. Databricks favors heavy transformation, streaming, semi-structured data, and ML on one platform; warehouses favor SQL-first analytics simplicity. Plenty of stacks run both with a clear division of labor.
Yes. We inventory the jobs, kill the zombies, add tests and monitoring to the survivors, and leave documentation so the next person is not archaeologist plus engineer.