Databricks lakehouse implementation for marketing and commerce data: Delta Lake pipelines, log-level processing at scale, and ML workloads on the same platform.
A lakehouse that became a swamp
Databricks is powerful enough to let a team make an expensive mess. Raw files land in cloud storage with no table format, so nothing is queryable without tribal knowledge. Notebooks become production pipelines with no version control, tests, or scheduling. Clusters are provisioned generously and never right-sized. Six months later there is a lot of data, a large bill, and no reliable reporting layer on top of it.
What we implement on Databricks
- Delta Lake medallion architecture with bronze, silver, and gold layers that actually mean something
- Large-scale processing of log-level DSP feeds, event streams, and clickstream data
- Structured Streaming pipelines where latency genuinely matters, batch where it does not
- Unity Catalog governance: lineage, access control, and discoverability
- ML pipelines and MLflow tracking on the same platform as the reporting data
- Cluster policies and job configuration tuned to keep spend proportionate
How we work
Establish the table format and medallion layout before adding pipelines
Move production logic out of notebooks into version-controlled, tested jobs
Build the gold reporting layer and connect BI to it, not to raw data
Right-size clusters and set policies, then review real cost after a month
Typical stack
Frequently asked questions
For most marketing reporting, Snowflake or BigQuery is simpler and cheaper, and we will say so. Databricks earns its complexity when you are processing log-level or clickstream data at genuine volume, need Spark for transformations a warehouse handles poorly, or want ML training and feature engineering on the same platform as reporting. Choosing it for ordinary ad reporting is overbuying.
Commonly, yes, and it is often the right answer. Databricks handles heavy processing and ML, then publishes modeled gold tables to Snowflake or BigQuery where BI tools and analysts are already productive. Each does what it is good at.
It gives every table an explicit contract: bronze is raw and immutable, silver is cleaned and conformed, gold is business-ready. Without that separation, teams transform in place, lose the ability to reprocess history, and cannot tell which tables are safe to build on.
Yes, and we often do. dbt handles the SQL transformation layers well and keeps modeling logic portable and reviewable, while Spark jobs handle what SQL cannot express efficiently.
Go deeper
Assess whether you need a lakehouse
Tell us your data volumes and workloads and we will tell you honestly whether Databricks is the right call.
Start a project