Databricks Development & Lakehouse Engineering

Databricks stores data as Delta Lake tables: open Parquet files with a transaction log that adds ACID transactions, schema enforcement and time travel. That lets one copy of the data serve data engineering, SQL analytics and machine learning, instead of maintaining a data lake for data scientists and a separate warehouse for BI.

The engineering discipline matters as much as the platform. Workspaces that grow organically end up with hundreds of untracked notebooks, all-purpose clusters left running and tables nobody owns. We structure work into version-controlled jobs, deploy them with CI/CD using Databricks Asset Bundles, and apply cluster policies so cost and access stay under control.

Unity Catalog is Databricks' governance layer: a single place to manage permissions, row and column level access, lineage and auditing across workspaces. If your tables still sit in the legacy per-workspace Hive metastore, upgrading to Unity Catalog is usually the first project we recommend, because most newer Databricks features depend on it.

Bronze, silver and gold layers are a convention, not a quality check in themselves. The pitfalls we see most often are pushing every source table through all three layers whether anyone uses it or not, applying business logic in bronze so raw data can no longer be replayed, and building dozens of near-identical gold tables, one per report. Each adds storage, compute and maintenance without adding clarity.

We keep bronze as an append-only record of what arrived, put cleansing, deduplication and conforming in silver, and model gold around shared business entities and metrics that several reports reuse. Incremental processing with Auto Loader and the Delta change data feed means each run touches only new or changed data, which is usually the biggest single lever on pipeline cost and run time.

Hadoop migrations typically move HDFS data to cloud object storage, convert Hive tables to Delta and port Spark or Hive jobs to Databricks jobs, retiring cluster administration along the way. Warehouse migrations from on-premises SQL platforms land raw data first, rebuild transformations in the medallion layers and reconcile outputs against the old system before reports are switched over.