An end-to-end accelerator for migrating legacy data environments (such as EDW, Hadoop, SAP analytics, and legacy SQL/ETL) to the Databricks Lakehouse quickly and reliably. It automates assessment, code conversion, pipeline orchestration, and validation while embedding governance, security, and FinOps best practices so organizations modernize analytics and ML workloads with reduced risk and accelerated time to value.
A governance-first solution that operationalizes unified data, ML, and AI governance across the Databricks platform. It embeds policies, lineage, access controls, auditing, and compliance guardrails into the Lakehouse architecture, enabling secure data discoverability, consistent enforcement of controls, and audit-ready workflows from day one.
A rapid, blueprint-driven accelerator that delivers a production-ready Databricks Lakehouse in about 30 days. It includes secure workspace setup, medallion ETL pipelines, Unity Catalog governance, and optional GenAI demos powered by vector search—giving teams a scalable foundation for analytics, BI, and AI initiatives in weeks rather than months.
A repeatable, governed framework for building and scaling AI on Databricks. This solution helps launch your first governed agent in under three weeks and provides automation, CI/CD integration, managed pipelines, model serving, and guardrails for ongoing AI delivery—reducing total cost of ownership and accelerating enterprise AI deployment with governance baked in.