Databricks, PySpark & Apache Spark consulting
Mid-market teams adopt Databricks when extracts, notebooks, and ad hoc SQL cannot keep up with ERP, CRM, and finance reporting. We build lakehouse layers and Spark pipelines that feed Power BI and Microsoft Fabric—not disconnected engineering experiments.
Book a discovery call See demo page AQ PulseGrid (Spark/Databricks)
What we deliver
- PySpark notebooks and jobs for bronze/silver/gold curation
- Delta Lake tables sized for operational and executive reporting
- Incremental loads, data quality checks, and job scheduling patterns
- Documentation and handoff so your team can extend pipelines
- Semantic models and dashboards on top—Power BI or Fabric as the consumption layer
Who it is for
Manufacturers, distributors, and B2B services firms that already run Databricks (or are piloting it) and need pipelines tied to the same KPI definitions finance uses in Power BI.
Example pattern
Nightly ERP and CRM extracts land in bronze Delta tables; PySpark jobs curate silver and gold layers at the grain finance expects; Power BI connects on top for executive and operational dashboards—migrated ERP extracts to Delta, trusted reporting above, not a second set of numbers.
How this fits Power BI and Fabric
Spark work succeeds when measures and entities match your reporting model. We align lakehouse tables with semantic model design so leadership does not get two versions of revenue, margin, or backlog.
Related
Microsoft Fabric for SMBs · ERP reporting modernization · Semantic model consulting · Services overview
Next step
Share your sources, refresh pain points, and one dashboard you wish you could trust—we will outline a practical lakehouse path.