Modern Data Platform Implementation

Poor data quality costs enterprises millions annually, and most organizations cannot adopt AI because their data is not ready. As a Databricks Partner, Dhristhi implements unified lakehouse architectures with Unity Catalog governance and AI-ready foundations built for adoption.

Platform Architecture & Strategy

Designing your modern data foundation

Lakehouse architecture, governance model, cost controls, operating model, and target-state roadmap.

• Lakehouse architecture with Delta Lake foundations
• Business-value alignment and platform KPIs
• Unity Catalog governance model
• FinOps and cost management strategy

Databricks Platform Implementation

Building your unified data intelligence engine

Databricks configuration, Unity Catalog, compute design, Delta Lake, SQL warehouses, and security controls.

• Workspace setup with cluster policies and security settings
• Delta Live Tables and ETL/ELT workflow foundations
• Unity Catalog access controls, lineage, and discovery
• Accelerator deployment for governance and cost visibility

Data Engineering & Pipeline Development

Automating data workflows

Reliable data pipelines, Delta Live Tables, quality gates, lineage, monitoring, and scalable orchestration.

• Declarative pipelines with data quality expectations
• Structured Streaming and Auto Loader patterns
• Automated data quality checks and remediation
• Bronze, silver, and gold medallion architecture

Analytics & ML Enablement

Empowering data-driven decisions

Self-service analytics, feature readiness, MLflow, MLOps foundations, and governed data products.

• Databricks SQL warehouses and BI integration
• MLflow experiment tracking and model registry
• Feature engineering and data science workspaces
• RAG, vector search, and generative AI readiness