BuildItAll is a European consulting firm specialized in helping small and mid-sized companies build scalable Data Platforms. After securing €20M in Series A funding, BuildItAll was approached by a Belgian e-commerce client who generates massive amounts of data daily and wanted to become more data-driven.
Our team was tasked with setting up a cost-optimal, scalable Big Data Processing platform based on Apache Spark on the cloud. This product demonstrates the proposed architecture and solution for enabling large-scale data ingestion, processing, and analytics capabilities for the client.
The platform is designed to efficiently handle big data workloads while staying true to BuildItAll's core value of building cost-effective cloud solutions. It uses AWS, Apache Spark, and Terraform to process large datasets efficiently. It is designed to be cost-effective, easy to maintain, and ready for client onboarding.
| Component | Purpose |
|---|---|
| CI/CD (GitHub Actions) | Automates code deployment and infrastructure updates |
| Infrastructure (Terraform | Automates cloud setup (IAM, S3, networking) |
| Orchestration (Apache Airflow) | Manages data pipeline workflows |
| Spark jobs (PySpark) | Simulates realistic e-commerce datasets and processing |
Clear architecture documentation outlining the platform’s design.
For Architecture documentation Click here
- Cloud: AWS (S3, IAM)
- Big Data Framework: Apache Spark
- Workflow Orchestration: Apache Airflow
- Infrastructure: Terraform
- Automation: GitHub Actions
- Programming Languages: Python, PySpark
For immediate use, refer to the documentation
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Scalable Big Data Processing Built with Apache Spark for seamless handling of large datasets. The platform supports both batch and real-time processing to meet dynamic business needs.
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Cost-Optimized Cloud Infrastructure Utilizes AWS to ensure efficient use of resources, optimizing costs without compromising performance. Built with Terraform for reproducible and version-controlled infrastructure.
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Modular and Maintainable Architecture Designed for easy scalability and maintainability, making future updates or onboarding new team members a smooth process. The modular setup ensures flexibility in adapting to evolving business requirements.
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Automated Data Pipelines Apache Airflow is used for orchestrating complex workflows, ensuring seamless data movement from raw to processed datasets, with monitoring and error handling built-in.
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Data Storage with AWS S3 Structured in raw and processed data zones on S3, ensuring efficient data storage and easy access for analytics or future processing.
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CI/CD Automation GitHub Actions for continuous integration and continuous deployment, enabling automated testing, builds, and deployment of code and infrastructure.
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Production-Ready Setup Designed with best practices to meet production-level requirements for performance, security, and maintainability, ensuring readiness for full deployment.
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Client Onboarding Ready Built to be easily understood and managed by the client with a focus on user-friendly maintenance and smooth adoption for future scaling.
- Infrastructure as Code (IaC): All cloud resources (S3 buckets, IAM roles, networking) are provisioned using Terraform, ensuring reproducibility, version control, and easy updates.
- Modular Code Structure: The repository is neatly organized into modules: infrastructure, orchestration, data generation, and CI/CD, improving maintainability and collaboration.
- Environment Separation: Clear separation between raw and processed data zones in AWS S3, following data lake design principles.
- Comprehensive documentations to support onboarding, repository cloning, and commercial deployment.