Practice 02 · Data engineering & data integration
Databricks consulting
Databricks gives data engineering, analytics and machine learning one lakehouse to share. As a Databricks consulting partner, we design that lakehouse, build the pipelines that feed it and make it dependable at scale.
Why Databricks
Unified.Scalable.Collaborative.
02 / Databricks
- Azure Databricks
- Lakehouse pipelines
- Unity Catalog governance
What Databricks is
Databricks is a data and AI platform built on the lakehouse: one architecture that combines the open, low-cost storage of a data lake with the reliability and performance of a data warehouse. Data engineers, analysts and data scientists work on the same data, under the same rules.
It runs on every major cloud. In Microsoft Azure it is offered as Azure Databricks, which is where many organizations use it alongside Data Factory, Data Lake Storage and Power BI.
Databricks rewards a deliberate design. Storage layout, naming, pipelines and permissions decided up front are what keep a lakehouse understandable when it holds years of data and serves dozens of teams.
Why choose Databricks
Three reasons organizations choose Databricks for their data platform.
Unified
One platform for data engineering, SQL analytics, data science and machine learning, governed in one place instead of stitched together.
Scalable
Compute scales with the work, from a small daily load to very large batch and streaming jobs, without re-architecting along the way.
Collaborative
Notebooks, SQL and shared tables let engineers, analysts and data scientists work on the same data with the same permissions.
Databricks work
Examples from our public record on Databricks.
Health system
EHR modernization on Databricks for an enterprise health system: modernizing how electronic health record data is stored, processed and served.
Government
Data integration and spatial mapping on Databricks for a large government agency, bringing separate sources together and placing them on the map.
Healthcare scale
Very large-scale data management on Databricks for healthcare work, where reliability and governance are not optional.
What we do on Databricks
From the first workspace to a platform your team runs on its own.
Lakehouse architecture
Medallion layers, storage layout and naming designed up front, so the platform stays understandable as it grows.
Data engineering and integration
Batch and streaming pipelines from your operational systems, built to be tested, monitored and rerun safely.
Governance and security
Catalogs, permissions and attribute-based access control set up so the right people see the right data, and nobody else.
Enablement
We work alongside your engineers and analysts and train them to own the platform, not only to use it.
Talk to a Databricks consultant
Starting on Databricks, or bringing order to a workspace that grew on its own? Tell us where you are.