Cloud Operations
Databricks
Lakehouse delivery on Databricks with governance, performance, and ML best practices.
Service snapshot
We implement Databricks platforms with Unity Catalog, Delta, and MLflow—ensuring governance, performance, and operational excellence across data and AI workloads.
- Workspace design, Unity Catalog, and security model setup.
- Delta Lake architectures with streaming and batch pipelines.
- ML lifecycle with Feature Store, MLflow, and deployment patterns.
Where we focus
What we deliver
- Workspace design, Unity Catalog, and security model setup.
- Delta Lake architectures with streaming and batch pipelines.
- ML lifecycle with Feature Store, MLflow, and deployment patterns.
- Cost optimization for clusters, jobs, and storage.
Proof of value
Outcomes you can expect
- Governed lakehouse with audited access and lineage.
- High-performance pipelines leveraging Delta and Spark.
- MLOps practices standardized on Databricks tooling.
- Cost-efficient clusters with right-sizing and policies.
How we work
Engagement building blocks
Each engagement combines strategy, build, and adoption. We leave your teams with the assets, playbooks, and operating rhythms needed to keep improving after launch.
Databricks
Governance
Unity Catalog, access models, and lineage to keep data secure and discoverable.
Databricks
Pipelines & performance
Delta architectures, structured streaming, and tuning for reliable throughput.
Databricks
ML lifecycle
Experimentation, registry, and deployment patterns with MLflow and Feature Store.
Ready to explore how Databricks can move the needle?
We’ll align on the outcomes that matter, assemble the right team, and start with a fast, low-risk path to value.