A Lakehouse, Sized for a Growing Business
Databricks is one of the platforms we know well. It's a good fit when you want reporting and AI on the same governed data. It isn't always the right choice, and we'll say so when it isn't.
One governed platform, every workload
From raw data to reports and AI applications, with lineage tracked the whole way.
The parts of Databricks we use most
Lakehouse
A single platform for BI, data science, and AI — eliminating duplicate copies and stale exports.
Delta Lake
ACID transactions, time travel, and schema enforcement on top of open Parquet storage.
Unity Catalog
Centralized access control, lineage, and audit across every workspace and workload.
Mosaic AI
Model serving, agent orchestration, and governed access to foundation models.
MLflow
Experiment tracking, model registry, and reproducible deployment pipelines.
Vector Search
Managed embeddings and retrieval infrastructure powering production RAG.
Lakeflow
Declarative, auto-scaling pipelines for ingestion and transformation at scale.
AI Functions
Call LLMs directly from SQL for classification, extraction, and summarization.
Feature Engineering
A governed feature store shared across models and teams for consistent inference.
Governance isn’t an afterthought
When we build on Databricks, we set up Unity Catalog from the start. It handles access rules, masking of sensitive columns, and lineage from raw data through to the dashboards and AI tools that use it. You can always see where a number came from and who can see it.
- Column and row-level access policies enforced centrally
- Automatic lineage from source tables to dashboards and agents
- PII/PHI masking applied consistently across every tool
- Full audit trail for every query and model inference
Thinking about a new data platform?
Tell us what you're running today and what's getting in the way. We'll help you figure out whether Databricks, or something simpler, makes sense.