The Batch Data Processing System is an advanced cloud computing project that combines Data quality checks and Batch jobs, built with dbt. The project follows a cloud-native architecture where resources are provisioned, scaled, and managed as code, making it reliable, cost-effective, and easy to reproduce. It showcases professional cloud engineering techniques while delivering a complete, demo-ready platform.
Managing this workload on traditional infrastructure is slow, expensive, and hard to scale. Without a cloud platform built on Batch jobs and dbt, there is no automated, resilient, and pay-per-use way to run the service reliably for growing demand.
This project applies advanced cloud engineering practices through Data quality checks, orchestrated with dbt and Batch jobs. The system is designed for automation, observability, and cost control, with security and resilience built in. It delivers consistent, scalable results and can be extended to additional cloud services and regions.
dbt
Spark
AWS / Azure / GCP
Docker / Kubernetes
Terraform / IaC
Serverless services
Monitoring and CI/CD
Kafka / Kinesis
Data Lake
Cloud-native system with Data quality checks and Batch jobs
Automated provisioning and scaling on demand
Observability with metrics, logs, and alerts
Security controls, encryption, and access management
Reusable cloud modules for related features
Cost-aware, documented, maintainable cloud architecture
The system follows a cloud-native architecture: the compute layer runs Data quality checks; the orchestration and logic layer uses dbt and Batch jobs; and the data and storage layer persists state via Transformation. Shared IaC, security, and observability modules support all layers, keeping the platform automated, resilient, and easy to extend.
Set up cloud account, project, and infrastructure as code.
Provision core resources and networking for Data quality checks.
Implement the workload logic using dbt and Batch jobs.
Add the data and storage layer via Transformation.
Wire up automation, monitoring, and cost controls.
Test scaling, failures, and security, then refine.
Document the architecture, run a demo, and prepare the viva report.
Architect cloud-native, scalable systems
Apply Stream and batch processing and Data lake architecture
Provision infrastructure with automation
Secure, monitor, and optimize cloud workloads
Present and defend a complete cloud project in viva
Add multi-cloud and hybrid deployment
Introduce advanced AI and analytics on the platform
Add disaster recovery and global replication
Optimize cost with predictive autoscaling
The Batch Data Processing System delivers a complete, production-grade cloud platform — from automated infrastructure to secure, scalable services and observability. It is practical, cost-effective, and easy to explain, making it an excellent final year project that demonstrates advanced cloud computing skills.
