Databricks & Lakehouse Engineering

Delta Lake pipelines, Unity Catalog, PySpark, and MLflow — built to production standards, not notebook demos.

Databricks gives you a lakehouse — but turning notebooks into production pipelines takes discipline. We build Delta pipelines, wire up Unity Catalog, and deliver PySpark and SQL jobs that are testable, documented, and cheap to run.

What we deliver.

  • Workspace and cluster policies configured for cost and security
  • Unity Catalog rollout — catalogs, schemas, grants, and external locations
  • Delta Lake pipelines (medallion: bronze, silver, gold) with PySpark or SQL
  • Delta Live Tables (DLT) pipelines where they fit
  • Job orchestration via Workflows or Airflow with retries and alerting
  • MLflow experiment tracking, model registry, and deployment patterns
  • Cost monitoring — cluster right-sizing, spot instances, and autoscaling rules

Use cases.

Notebook-to-production migration

You have analysts running notebooks manually. We refactor the logic into proper jobs, add tests, and set up scheduled runs with alerting.

Unity Catalog migration

Moving from Hive metastore to Unity Catalog. We plan the cutover, migrate tables and grants, and update downstream consumers with minimal disruption.

Greenfield lakehouse

Starting from S3 or ADLS with no lakehouse yet. We design the medallion layout, build the first bronze/silver pipelines, and hand over a working template.

ML platform bootstrapping

Data scientists want MLflow but nobody has set it up. We configure experiment tracking, the model registry, and a deployment path to real-time or batch serving.

Questions.

Do you work with Azure, AWS, or GCP Databricks?+
All three. Azure and AWS are the most common in our work; GCP comes up occasionally. The lakehouse patterns are the same — only the storage and IAM wiring differ.
PySpark or SQL?+
Whichever makes the code easier for your team to own. SQL for straightforward transforms, PySpark when you need real logic, testing, or reuse. We default to whichever your team is already fluent in.
Can you work alongside our existing data team?+
Yes. Most engagements are collaborative — we pair with your engineers, leave clean code and docs, and avoid the consultant-leaves-and-everything-breaks pattern.
Do you handle streaming as well as batch?+
Yes — Structured Streaming and DLT for near-real-time pipelines. That said, we will push back if batch is the right answer. Streaming has real operational cost.

Ready to start?

Tell us what you are building. We will reply within 24 hours with a few questions and a clear next step.

Get in touch[email protected]