This repository presents the architecture, design decisions, and implementation blueprint of a production-grade Modern Data Platform.
It demonstrates end-to-end capabilities across:
- Serverless Data Warehousing (AWS)
- Multi-Cloud Data Pipelines
- Self-Service Analytics
- Augmented Analytics with LLMs
- Data APIs
- Governance, Observability & Security
- Databricks Lakehouse Architecture
- 7-Layer Modern Data Stack Implementation
This is not a collection of isolated pipelines —
this is a scalable, enterprise-ready data platform architecture.
flowchart LR
A[Operational Systems / SaaS / Streaming] --> B[Ingestion Layer]
B --> C[Raw Layer]
C --> D[Bronze Layer]
D --> E[Silver Layer]
E --> F[Gold Layer]
F --> G[Semantic Layer]
G --> H[Consumption Layer]
F --> I[Data APIs]
E --> J[AI / LLM Layer]
C --> K[Governance & Observability]
D --> K
E --> K
F --> K
Designed as a scalable data platform, not a pipeline collection.
Serverless-first, decoupled storage/compute, auto-scaling by design.
All infrastructure reproducible, versioned, environment-isolated.
Data quality, lineage, RBAC and monitoring are core components — not add-ons.
LLMs and augmented analytics integrated into the platform lifecycle.
Design a fully serverless, elastic, cost-efficient data warehouse architecture using Medallion principles.
flowchart LR
A[Databases / APIs / Files] --> B[Ingestion Service]
B --> C[Raw Storage]
C --> D[Bronze Transform]
D --> E[Silver Clean]
E --> F[Gold Aggregations]
F --> G[Serverless Query Engine]
G --> H[BI / Consumers]
- Partitioning strategies
- Late-arriving data handling
- Schema evolution
- Cost-aware storage tiering
- Workload isolation
- Query performance optimization
✔ Horizontal scalability
✔ Cost elasticity
✔ High availability
✔ Secure access control
Build cloud-agnostic, version-controlled pipelines with CI/CD and environment isolation.
flowchart LR
A[Git Repository] --> B[CI Pipeline]
B --> C[Unit & Data Tests]
C --> D[Build Artifacts]
D --> E[Deploy to Dev]
E --> F[Promote to Prod]
- Metadata-driven orchestration
- Parameterized infrastructure
- Automated testing
- Rollback & promotion strategies
- Environment segregation (Dev / Test / Prod)
Enable governed, scalable, business-driven analytics.
flowchart LR
A[Gold Layer] --> B[Semantic Layer]
B --> C[Metrics Standardization]
C --> D[BI Tools]
B --> E[Row-Level Security]
- Star schema modeling
- Semantic layer abstraction
- Centralized metrics definitions
- Performance optimization strategies
- Cost governance enforcement
Integrate AI into the analytics workflow.
flowchart LR
A[User Question] --> B[LLM Prompt Layer]
B --> C[Metadata Retrieval]
C --> D[SQL Generation]
D --> E[Query Execution]
E --> F[Insight Generation]
F --> G[User Response]
- Natural Language to SQL
- Retrieval-Augmented Generation (RAG)
- Semantic metadata embeddings
- Conversational analytics
- Controlled prompt engineering
- AI-assisted data discovery
Expose curated datasets securely through versioned APIs.
flowchart LR
A[Gold Layer] --> B[API Service]
B --> C[Authentication / RBAC]
C --> D[Rate Limiting]
D --> E[External Systems]
- RESTful architecture
- API versioning
- Contract-first development
- Authentication & authorization
- Monitoring & throttling
- Observability integration
- Data Quality validation framework
- Freshness monitoring
- Volume anomaly detection
- Schema drift detection
- Lineage tracking
- Role-based access control
- Policy enforcement
flowchart LR
A[Data Layers] --> B[Quality Checks]
A --> C[Freshness Monitoring]
A --> D[Lineage Tracking]
A --> E[Access Control Policies]
✔ Security by design
✔ Compliance-ready architecture
✔ Data trust as a platform capability
Build unified batch + streaming analytics using a Lakehouse architecture.
flowchart LR
A[Streaming Sources] --> B[Bronze Delta]
B --> C[Silver Delta]
C --> D[Gold Delta]
D --> E[ML / BI / APIs]
- ACID guarantees with Delta Lake
- Structured Streaming pipelines
- Z-Ordering & partition tuning
- Cost-performance optimization
- Scalable distributed compute
| Layer | Responsibility |
|---|---|
| 1 | Data Sources |
| 2 | Ingestion |
| 3 | Raw Storage |
| 4 | Transformation |
| 5 | Semantic Modeling |
| 6 | Consumption |
| 7 | Governance & Monitoring |
This platform was designed considering:
- Distributed systems scalability
- Fault tolerance & recovery
- Cost-performance trade-offs
- Infrastructure reproducibility
- High availability
- Security isolation
- Observability standards
- Maintainability at scale
- Cloud-native architecture
- Serverless compute
- Lakehouse architecture
- CI/CD for Data Engineering
- Infrastructure as Code
- Metadata-driven pipelines
- Data contracts
- API-first architecture
- LLM integration in data systems
- Enterprise governance patterns
This repository demonstrates readiness for:
- Senior Data Engineer
- Staff Data Engineer
- Principal Data Engineer
- Data Platform Architect
- Cloud Data Architect
- AI Data Architect
- Clear separation of concerns
- System design maturity
- Scalability trade-off awareness
- Governance-first architecture
- AI-native data integration
- Production-grade thinking
- Multi-cloud abstraction strategy
Open to discussions about system design, trade-offs, scalability patterns, platform engineering, and AI-enabled data architectures.
Consider starring it and connecting for architectural deep dives.