Data Platform
Building enterprise AI on Databricks
Data engineering, data science, and machine learning on one governed lakehouse. We use Databricks where custom ML and large-scale data pipelines need to live together.
What role does Databricks play in AI and analytics?
Databricks is a strong fit when data engineering, data science, and machine learning need to operate on the same governed data platform rather than across disconnected tools. Divine AI Systems uses Databricks in the Knowledge & Data layer of a client's architecture for large-scale data pipeline work, custom machine learning model development, and AI features that need direct access to well-governed lakehouse data.
Where It Fits
Where Databricks fits in the architecture
Databricks typically sits underneath the AI layer, doing the data preparation and model work that AI features depend on.
Knowledge & Data
- Lakehouse
- structured and unstructured data at scale
AI & Reasoning
- Custom ML training
- tracking
- serving
Orchestration & Integration
- Workflows (jobs)
- data pipelines
Cloud & Infrastructure
- Azure
- AWS
- Google Cloud
Security, Monitoring & Governance
- Unity Catalog
- data lineage
What We Build
What we build on Databricks
The range of what a Databricks-based engagement typically includes.
Custom Machine Learning Models
Predictive and classification models trained, tracked, and served on the lakehouse.
Large-Scale Data Pipelines
Ingestion and transformation pipelines feeding analytics and AI from governed data.
Retrieval Pipelines for AI Applications
Preparing and indexing lakehouse data for use in retrieval-grounded AI systems.
Data Governance & Lineage
Unity Catalog-based access control and lineage tracking across data assets.
When It Makes Sense
When Databricks is the right choice
These are the conditions where Databricks tends to be the stronger fit.
Data science and ML need to run on the same platform as data engineering
Avoids handoff friction between teams working on the same underlying data.
You're training custom models, not just calling an API
Databricks is built for the full model lifecycle, from experimentation to production serving.
Data volume and complexity exceed a standard warehouse
The lakehouse handles structured and unstructured data at a scale simpler platforms struggle with.
Tradeoffs
What to weigh before committing
These are the considerations we walk through with clients before recommending Databricks.
More platform than a typical BI-only need requires
Organizations wanting only dashboards and reporting may be better served by a simpler analytics tool.
Requires data engineering skill to operate well
Getting full value takes a team comfortable with pipelines and ML lifecycle tooling, not just SQL.
Cost scales with compute usage
Cluster sizing and job scheduling need deliberate management to keep cost predictable.
Related Services
Services we typically pair with Databricks
Related Solutions
Business problems this often supports
FAQ
Questions about building on Databricks
We build on Databricks for client engagements. We describe that as technical experience, not a formal partnership or certification.
Probably not — for reporting-only needs, a dedicated BI tool is usually a simpler and more cost-effective fit.
Yes — it's a common pattern to prepare and index lakehouse data in Databricks, then serve it to a retrieval layer for an AI application.
It runs on top of Azure, AWS, or Google Cloud — we'll typically match this to whichever cloud platform your organization already uses.
Working with large or messy data today?
Tell us what your data environment looks like, and we'll show you where a unified data-and-ML platform actually helps.