Big Data Processing on the Cloud

This project focuses on the application of Big Data processing in cloud environments, with an emphasis on using distributed computing tools like PySpark to handle massive datasets. The objective is to migrate local data processing workflows to the cloud, enabling scalability and efficiency for large-scale data operations. Through this project, I've learned the fundamental skills necessary for handling big data, including cloud architecture design, distributed data processing, and the integration of cloud services for data storage and computation.

Key Skills Acquired:

Big Data Processing: Leveraging PySpark to perform distributed data processing on large datasets in the cloud.
Cloud Computing: Understanding and utilizing cloud environments for data storage and computation (AWS, Google Cloud, etc.).
Data Architecture Design: Designing scalable and efficient architectures for processing big data in cloud environments.
Distributed Computing: Implementing distributed systems to perform calculations and handle large datasets efficiently.
Cloud Tool Integration: Working with cloud-native tools for data management, storage, and processing (e.g., cloud storage services, data lakes).
Data Migration: Transitioning data workflows from local environments to the cloud for scalability.

Technologies Used:

PySpark: Distributed data processing for big data analysis and computation.
Cloud Platforms: Amazon Web Services (AWS) for data storage and processing.
Big Data Tools: Tools for managing large datasets, including cloud storage solutions, data lakes, and processing engines.
Python: Data manipulation, integration with cloud tools, and automation of workflows.