Lakehouse foundation
Design and implement Databricks architectures that unify batch and streaming workloads with strong governance.
Operationalize data and AI at scale on the lakehouse with governed pipelines, analytics, and production ML.
Talk to usDatabricks
Caystard Group helps enterprises build a data intelligence platform on Databricks so analytics, AI, and business teams share one trusted foundation instead of fragmented tooling.
Design and implement Databricks architectures that unify batch and streaming workloads with strong governance.
Build reliable pipelines, quality checks, and orchestration that keep downstream teams productive.
Deliver curated datasets and semantic layers that make self-serve insight practical and consistent.
Move models and RAG systems into monitored production with clear ownership and evaluation loops.
Bring scattered warehouses and notebooks onto a coherent Databricks operating model.
Optimize cost, workspace standards, and access patterns so the platform stays healthy as usage grows.
Reduce handoffs between data producers and consumers with shared lakehouse patterns.
Pair experimentation with production controls so AI initiatives leave the pilot stage.
Give teams freedom to explore without losing lineage, access control, or auditability.
Cut duplicate pipelines and idle clusters through standards, automation, and FinOps habits.
Programs are scoped around decisions, products, and operating metrics, not notebook volume.
We connect Databricks cleanly to AWS, Azure, and GCP estates already in place.
We leave teams with standards, ownership, and practices that keep the platform evolving.
Partner with Caystard Group to implement, scale, and operationalize Databricks for analytics and AI.