Inspiration
What it doesData Analysis & Visualization: I gained valuable experience in using Pandas for data preprocessing, NetworkX for creating and analyzing graphs, and Matplotlib for visualizing graph structures.
How I Built the Project Dataset Collection & Preprocessing: I began by gathering SpaceX launch data from publicly available sources. The data was then cleaned and structured into a Pandas DataFrame for easy manipulation. Storing Data in ArangoDB: The preprocessed data was stored in ArangoDB, with each component (e.g., rockets, payloads, launch sites, missions) represented as a node in the graph. Creating Graph Structure & Relationships: I established relationships between the nodes (e.g., which rocket was launched from which site, which payload was used for each mission) to visualize how these elements are connected. Graph Analysis & GPU Acceleration: By leveraging AQL for graph queries and cuGraph for GPU-accelerated processing, I was able to efficiently analyze large datasets and extract meaningful insights about launch patterns. Visualization: Finally, I used NetworkX and Matplotlib to visualize the relationships in the graph and showcase launch trends and connections. Challenges Faced Data Quality & Completeness: One of the biggest challenges was dealing with missing or incomplete data in the SpaceX dataset. I had to find creative ways to clean and preprocess the data to ensure accuracy in the graph analysis. Scaling with Large Datasets: As the dataset grew in size, ensuring efficient graph traversal and analysis became difficult. This is where the integration of cuGraph and GPU acceleration proved to be invaluable, allowing me to process large datasets quickly and efficiently. Complexity of Graph Relationships: Building a meaningful graph structure required a deep understanding of how different components, such as rocket types, launch sites, and missions, are interconnected. It took time to model the data properly and define the right relationships. Conclusion This project was a great learning experience that allowed me to explore graph-based analysis, work with ArangoDB, and experiment with GPU-accelerated processing. It provided valuable insights into the launch patterns of SpaceX, and I’m excited about the potential applications of graph theory in analyzing complex data like this. Moving forward, I aim to expand the project by incorporating real-time data and adding more advanced analysis features.
Built With
- acceleration:
- api
- apis:
- aql
- arangodb
- built-with:-languages:-python-frameworks-&-libraries:-pandas
- cloud
- cugraph
- database
- database:
- for
- gpu
- gpu-accelerated
- graph
- language)
- language:
- matplotlib
- n/a
- networkx
- processing)
- query
- services: