Databricks consulting for lakehouse pipelines that feed trusted BI
Aurea Quantra helps mid-market teams use Databricks and Spark practically—Delta lakehouse layers, maintainable PySpark jobs, and gold tables Power BI can consume without orphan pipelines.
Databricks-certified practitioner delivery — focused on maintainable lakehouse patterns and BI handoff.
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Problems this engagement is built for
- Notebooks proliferate without bronze/silver/gold ownership
- Power BI still points at fragile SQL extracts
- Spark jobs are slow, costly, or undocumented
- Data science and BI teams do not share definitions
Architecture and integration patterns
- Medallion-style layers with clear producers and consumers
- Delta tables designed for analytical grain
- Job scheduling, observability, and handoff docs for analysts
- Semantic model contracts for Power BI (and Fabric when relevant)
Typical engagement
Lakehouse readiness review, then a phased plan and first governed pipeline/model that supports an executive or operational use case.
Starting deliverables
- Databricks/lakehouse readiness assessment
- Pipeline and table design for a priority subject area
- PySpark/Spark job implementation when scoped
- Power BI consumption handoff
Related platforms and pages
Databricks, PySpark & Spark · Power BI consulting · Microsoft Fabric consulting
Honest scope boundaries
- Certification means practitioner credentialing—not a claim that Databricks endorses Aurea Quantra as a partner unless separately stated
- We prioritize BI-connected lakehouse work over open-ended research clusters
FAQ
Are you Databricks-certified?
Yes. Paul Rendleman is Databricks-certified and applies that skill to mid-market lakehouse and BI delivery.
Do you only do Databricks?
No. Databricks is one strong option alongside Fabric, Snowflake, Redshift, and SQL—chosen for fit.
Ready to scope the first phase?
Bring the current stack and the decision your team cannot make confidently. The BI Diagnostic stays practical and vendor-aware—not a product pitch.
Disclaimer: Platform names identify technologies we can assess, integrate, or support. They do not imply vendor partnership unless explicitly stated.