Welcome to our Database Comparison Project! This project aims to compare the performance and characteristics of various databases—SQL, Cassandra, Neo4j, MongoDB, and Redis—under different data set conditions. By generating datasets using Python at different percentages (100%, 75%, 50%, 25%), we analyze and evaluate each database's behavior to determine the most suitable choice for different library use cases.
- Generate datasets using Python at varying percentages.
- Compare the performance of SQL, Cassandra, Neo4j, MongoDB, and Redis databases.
- Analyze the behavior of each database under different data set conditions.
- Provide insights and recommendations based on the comparison results.
For detailed information regarding our methodologies, experimental setup, results, and conclusions, please refer to the project report located within the project repository.
- Python: We utilized Python programming language to generate datasets and conduct database comparisons.
- SQL: We used SQL databases for structured data storage and analysis.
- Cassandra: Cassandra was employed for its scalability and high availability for distributed data storage.
- Neo4j: Neo4j was used for graph database representation and analysis.
- MongoDB: MongoDB was chosen for its flexibility in handling unstructured and semi-structured data.
- Redis: Redis served as an in-memory data store for caching and high-speed data retrieval.
To run the project and reproduce the comparison results:
- Clone the repository from GitHub.
- Set up each database according to its documentation and requirements.
- Generate datasets using the provided Python scripts.
- Execute the database comparison scripts.
- Analyze the comparison results and draw conclusions based on your requirements.
We welcome any feedback or suggestions regarding our database comparison project. If you have any questions or encounter any issues, feel free to contact.
Thank you for your interest in our project!
Happy comparing!