Welcome to my repository documenting my learning journey through various big data and cloud computing technologies. This collection encapsulates my hands-on experience with industry-standard tools and frameworks such as Docker, Kubernetes, Apache Spark, Apache Hadoop, and many others. Each technology is explored through practical implementations and projects that enhance my understanding and skills in big data processing, distributed computing, and scalable cloud deployments. Overview
This repository serves as a portfolio showcasing my skills and understanding of different technologies critical to modern big data and cloud computing. Each technology is covered through dedicated projects or code snippets, reflecting practical use cases and best practices in the field. Technologies Covered
- Docker: Containerization of applications to ensure consistency across multiple development and release cycles.
- Kubernetes: Orchestration of containerized applications to achieve high scalability and availability.
- Apache Spark: In-memory cluster computing for processing large-scale data operations efficiently.
- Apache Hadoop: Framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
- Elasticsearch: Real-time distributed search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured.
- Redis: In-memory data structure store, used as a database, cache, and message broker, for high-performance operations.
- Cloud Computing and deployment: Utilizing cloud infrastructure to enhance the flexibility and scalability of applications.
- Music Recommendation System:
- redis and elasticsearch
- api and cloud deployment
- Recommandation system as a service
- RabbitMQ
- Shezam.api
Finall project: kubernetes as a Service(kaas) cloud-computing-kubernetes-final-project