Tables with a contract
Bronze, silver and gold layers that actually mean something.
Databricks lakehouse
Databricks for marketing data at genuine scale.
Databricks earns its place when the data gets big or the workload gets scientific: log-level programmatic feeds, event streams, and models trained on the same data that feeds reporting. We build lakehouse pipelines on Delta Lake and keep them cheap enough to run daily.
- Delta Lake
- Log-level scale
- ML on the same data
What you get
A lakehouse, not a swamp.
Log-level data, processed daily
DSP feeds, event streams and clickstream at volume.
ML on the reporting data
MLflow-tracked models on the same platform as reporting.
Spend kept proportionate
Cluster policies and job configuration tuned to the workload.
The problem
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
What's included.
- 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
How we run it.
Step 1
Establish the table format and medallion layout before adding pipelines
Step 2
Move production logic out of notebooks into version-controlled, tested jobs
Step 3
Build the gold reporting layer and connect BI to it, not to raw data
Step 4
Right-size clusters and set policies, then review real cost after a month
What we use
Tools, platforms and methods.
- Databricks
- Delta Lake
- Apache Spark
- Unity Catalog
- MLflow
- dbt
- Python
Related work
Projects we've delivered.
FAQ
Common questions.
Do we need Databricks or would a warehouse do?
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.
Can Databricks and a warehouse coexist?
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.
Why does the medallion architecture matter?
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.
Can you run dbt on Databricks?
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.
Related
Related services.
Programmatic & CTV
DSP and log-level ingestion, cross-screen reach and CTV incrementality.
Learn moreSnowflake for marketing data
Multi-tenant access, dbt modeling and a predictable credit bill.
Learn moreMarketing data warehouse
dbt on Snowflake, BigQuery or Redshift: every ad platform modeled, tested and documented.
Learn moreAssess whether you need a lakehouse.
Tell us your data volumes and workloads and we will tell you honestly whether Databricks is the right call.