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AWS S3

My reusable templates. I hate writing code twice 😝

This Project is a comprehensive example project that demonstrates how to fully automate the build, test, and deployment process for a web application using React and Vite, orchestrated by GitLab CI/CD. The project covers the entire development lifecycle, including code quality checks, reproducible builds, automated unit and end-to-end testing, and multiple deployment strategies such as Netlify (for review, staging, and production environments), AWS S3, and AWS EC2 with Nginx. All credentials and secrets are managed securely using GitLab CI/CD variables, ensuring best practices for security and maintainability. The repository also includes advanced pipeline features like job templates, artifact management, parallelization with needs, and robust error handling. This project serves as both a practical template for professional CI/CD pipelines and a learning resource for modern DevOps workflows.

Stream files to AWS S3 using multipart upload.

Personal project portfolio and résumé

The project use traffic data from automatic measurement and corresponding weather data in order to support analyses aimed at answering the question: Do weather conditions impact traffic?

The aim of the project is to design and implement entire data data flow using ETL/ELT tools and methods. The steps include: csv data extract and initial processing (Python), load to AWS S3 Data Lake (Python boto3, AWS CLI), staging (Snowflake) and transformation (dbt core), data warehousing (Snowflake), data prep and EDA (Python, pandas) and visualisation (Streamlit).

AWS S3 Bot

Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet.  

Major cloud providers:

Amazon Web Services (AWS): The largest and most comprehensive cloud provider, offering a wide range of services from compute and storage to machine learning and analytics. Google Cloud Platform (GCP): Known for its focus on data analytics, machine learning, and artificial intelligence, GCP is a popular choice for businesses that need powerful tools for data-driven insights. Microsoft Azure: A strong competitor to AWS, Azure offers a wide range of services and is particularly well-suited for businesses that already use Microsoft products. Examples of cloud computing services:

Compute: Virtual machines, serverless computing, containers Storage: Object storage, block storage, file storage Databases: Relational databases, NoSQL databases, data warehousing Networking: Virtual private clouds, load balancing, content delivery networks Machine learning: Training and deployment of machine learning models Analytics: Big data processing, data warehousing, business intelligence By using cloud computing, businesses can scale their resources up or down as needed, reduce costs, and improve flexibility.

Configure aws-sdk-go for R2

Serverless App that runs a lambda on S3 upload

Sample asset management application for Toon City Animation (studio). Technologies used: Bootstrap, Javascript, Laravel with AWS S3 and ClearDB.

Project Link : http://tooncity-asset-management.herokuapp.com/