Azure Databricks

Overview

We design Azure Databricks workspaces for batch and streaming work: cluster policies, job clusters rather than all-purpose clusters left running, and Delta Lake tables with a partition scheme you can explain. PySpark, SQL, and notebooks are used where each fits the person who will maintain the job.

We wire Unity Catalog or the workspace catalogue you are ready for, plus job alerts and a promotion path from dev to production. Streaming jobs get a checkpoint location and a documented replay step.

What we deliver

• Workspace, cluster policies, and job clusters for scheduled work
• Delta Lake tables with partition and merge patterns for the main entities
• Production jobs with failure alerts and a rerun step
• Catalogue grants so analysts read curated tables without workspace admin rights

Business benefits

• Large transforms run on a schedule instead of on someone's laptop
• Job clusters stop when the work finishes, which keeps the bill predictable
• Corrected source data can be replayed for one partition