Modern Serverless Data Platform
A modern data lakehouse architecture leveraging Apache Iceberg and AWS services to create a scalable, serverless data platform with automated deployment and governance.
Overview
Engineered a modern data platform leveraging Apache Iceberg and AWS services to create a scalable, serverless data lakehouse architecture. The platform implements a Bronze-Silver-Gold data paradigm with automated deployment through GitHub Actions and comprehensive data governance using AWS Lake Formation.
Architecture Components
- Data Ingestion Layer
- AWS RDS (MySQL and PostgreSQL) as source systems
- AWS Glue ETL jobs for data extraction and loading
- AWS Cloud Development Kit (CDK) for infrastructure deployment
- Data Processing Layer
- Apache Iceberg for table format management
- AWS Lake Formation for data access control
- AWS Glue Data Catalog for metadata management
- AWS Athena with Spark workgroups for data processing
- CI/CD Pipeline
- GitHub Actions for automated deployment
- Docker for containerization
- AWS ECR for container registry
- AWS ECS for container orchestration
- Data Transformation
- dbt for data transformations
- SparkSQL for data processing
- AWS Fargate for serverless compute
Technical Implementation
Data Layer Architecture
The platform implements a multi-layer data architecture that aligns with dbt best practices:
| Data Lake Layer | dbt Layer | Description | Implementation Details |
|---|---|---|---|
| Bronze | Raw | Initial ingestion layer where raw data from source systems is loaded with minimal or no transformation | • Raw data loaded from source systems (RDS MySQL/PostgreSQL) |
| Silver | Staging | Cleaned and standardized data layer, focused on removing duplicates, standardizing formats, and generally improving data quality | • Automated data cleaning pipelines |
| Silver | Intermediate | Layer that holds enriched data and additional transformations to create relationships between entities, often used for further downstream aggregations | • Entity relationship mapping and joins |
| Gold | Facts | Highly curated layer representing analytics-ready data in a fact table format, typically containing metrics or aggregations for specific use cases | • Core metric calculations and aggregations |
| Gold | Marts | Final layer, designed for specific business domains and reporting, tailored to business requirements or specific use cases | • Domain-specific data models |
Key Features
The modern data platform provides a robust foundation for data operations through:
-
Serverless Architecture
- Pay-per-use compute resources
- Automatic scaling
- Minimal operational overhead
-
Data Quality and Governance
- Automated data validation with dbt tests
- Fine-grained access controls and column-level security
- Data discovery and cataloging
- Automated schema evolution
-
Performance Optimization
- Query optimization with Apache Iceberg
- Partition management
- Compute resource optimization
-
Automated Deployments
- GitHub Actions for CI/CD with automated testing, validation, and Docker image deployment
- AWS CDK for Infrastructure as Code, ensuring consistent, version-controlled infrastructure
- Environment provisioning with automated resource management
-
Increased Development Efficiency
- Standardized data models across projects
- DataOps-driven CI/CD pipeline
- Auto-generated documentation via dbt models
Technical Outcomes
- Implemented end-to-end data pipeline automation aligned with dbt best practices
- Established data quality frameworks across transformation layers
- Created maintainable and version-controlled data transformations
- Built modular infrastructure supporting iterative development