Databricks-Centric Lakehouse Architecture

 Case Study 

Existing Application to a Databricks-Centric Lakehouse Platform

Executive Summary

The current application architecture relies on a multi-layered Azure-based setup involving React App Service, Azure Functions, and Cosmos DB for data ingestion, processing, and visualization. While functional, this design introduces unnecessary complexity, operational overhead, and multiple points of dependency.

This proposal outlines a modernized, simplified, and scalable architecture leveraging Databricks Apps, Unity Catalog, Delta Lake, and Databricks AI (Genie) to streamline the system into a unified platform. The proposed approach reduces service dependencies, enhances governance, improves performance, and enables native AI-driven insights.

Current Architecture Overview

The existing solution consists of the following components:

• Frontend Application (React JS) hosted on Azure App Service
• Azure Functions acting as middleware for:
• Communication with Databricks / ADLS
• Communication with Cosmos DB
• Notification handling
• Cosmos DB used for:
• Data ingestion and storage
• Querying for visualization
• Databricks / ADLS accessed indirectly via Azure Functions

Key Functional Capabilities

• Capture user inputs from frontend
• Update JSON payloads
• Processed Input data based on updated JSON
• Ingest data into Cosmos DB
• Retrieve data for visualization
• Send user notifications

Challenges with Current Architecture

The current design introduces several limitations:

• High Dependency Chain
• Tight coupling between frontend, Azure Functions, Cosmos DB, and Databricks
• Operational Complexity
• Multiple services to maintain and monitor
• Increased DevOps overhead
• Performance Overhead
• Multiple network hops between services
• Increased latency for data access
• Governance Fragmentation
• Data access control spread across services
• Limited centralized governance
• Limited AI Enablement
• Minimal integration with advanced analytics and AI capabilities

Proposed Architecture

Proposed transitioning to a Databricks-centric unified architecture that consolidates application, data, and AI capabilities into a single platform.

Core Components

 Databricks Apps (Frontend Layer)

• Replace Azure App Service + Azure Functions
• Provide UI for:
• Capturing user input
• Managing JSON data
• Rendering visualizations

Delta Lake on ADLS (Data Layer)

• Replace/augment Cosmos DB
• Store:
• Structured data (Delta tables)
• Semi-structured JSON data 

 Unity Catalog (Governance Layer)

• Centralized control for:
• Data access (RBAC/ABAC)
• Data lineage
• Security policies 

 Databricks SQL Warehouse (Query Engine)

• High-performance query execution
• Enables dashboards and app-driven queries

 Databricks AI / Genie (Optional Layer)

• Natural language querying (NL → SQL)
• AI-driven insights and summarization

 Databricks Dashboards

• Replace custom-coded visualization logic
• Provide governed, reusable visual reporting

Proposed Functional Flow

User → Databricks App (SSO via Entra ID)

     → Direct interaction with Delta Tables (via SQL Warehouse)

     → Unity Catalog enforces access controls

     → Data stored/retrieved from ADLS (Delta + JSON)

     → Visualization via built-in dashboards or app UI

     → Optional: AI-driven insights via Genie

Key Improvements

 1. Reduced Dependency Footprint

• Eliminates:
• Azure Functions
• Intermediate API layers
• Reduces system complexity

 2. Unified Data Platform

• Single platform for:
• Data ingestion
• Storage
• Processing
• Visualization
• AI

 3. Enhanced Governance

• Centralized through Unity Catalog:
• Fine-grained access control
• Auditability
• Data lineage

 4. Improved Performance

• Direct data access (no intermediaries)
• Optimized query execution via SQL Warehouse
• Reduced network overhead

 5. Cost Optimization

• Elimination of:
• Cosmos DB RU provisioning
• Azure Function execution costs
• Pay-per-use model with serverless compute

 6. Native AI Enablement

• Use Databricks Genie to:
• Enable natural language interactions
• Generate insights without manual queries
• Reduce need for custom analytics logic

7. Simplified Visualization Strategy

• Replace custom graph rendering with:
• Databricks Dashboards (no/low code)
• Maintain flexibility via:
• Optional custom visualization (Plotly/Streamlit)

Expected Outcomes

Area	Impact
Architecture complexity	⬇ Reduced significantly
Performance	⬆ Improved
Cost	⬇ Optimized (20–50%)
Governance	⬆ Centralized
Maintainability	⬆ Simplified
AI capability	⬆ Enable

        Key Points:

            Centralized Secure Architecture:

                        A unified, identity-driven Databricks centric security framework enables proactive                                    risk management and business scalability.

            Security as Business Enabler:

                        Transforming security from a reactive role into a proactive driver of trust and growth                                 supports business objectives.

            Investment in Resilience:

                        A modern, scalable, and AI-enabled Lakehouse solution is an investment yielding long-                            term confidence and sustainable success.

High-level design

Trade-offs / Considerations

Area	Consideration
Frontend flexibility	Databricks Apps less mature than full React
Cosmos DB	Keep only if low-latency transactional workloads needed
Skill shift	Teams need Databricks-centric skills
Vendor lock-in	More reliance on Databricks ecosystem

This workload is data engineering (ingestion + validation → gold), NOT an app / BI / interactive querying workload.  Therefore, we cannot completely avoid compute (Databricks cluster or equivalent) But we can replace traditional clusters with more cost-efficient options.

Databricks Notebook fetch data from ADLS and Event Hub, therefore ingestion layer and process layer is separate and independent. Data can be ingested continuously, and Databricks job can run batch by batch in a day from Monday to Friday, if business permits and that can reduce cluster cost. Second option is to use job cluster; however, job cluster will take time to initialize and installed libraries to be ready to process.

Conclusion

The proposed re-architecture transforms the current system into a modern, scalable, and AI-enabled Lakehouse solution. By consolidating multiple services into Databricks, the organization can achieve:

• Reduced operational overhead
• Improved performance and scalability
• Stronger data governance
• Enhanced user experience with built-in AI capabilities

This approach aligns with enterprise best practices for data platforms and provides a future-ready foundation for advanced analytics and intelligent applications.