LynxKite is a powerful analytics tool for very large graphs and other datasets.
It scales to billions of edges thanks to the underlying Apache Spark cluster computing engine.
It seamlessly combines the benefits of a friendly graphical interface and a powerful Python API.
- Hundreds of scalable graph operations, including graph metrics like PageRank, embeddedness,
and centrality, machine learning methods, graph segmentations like modular clustering, and various
transformation tools like aggregations on neighborhoods, filtering, and SQL queries.
- The two main data types are graphs and relational tables. Switch back and forth between the two
as needed to describe complex logical flows.
- A friendly web UI for building powerful pipelines of operation boxes. Define your own custom boxes
to structure your logic.
- Everything is accessible through a simple Python API as well.
- Integrates with the Hadoop ecosystem. Import and export from CSV, JSON, Parquet, ORC, JDBC, Hive,
- Fully documented.
- Proven in production on large clusters and real datasets.
- Fully configurable graph visualizations and statistical plots. Experimental 3D and ray-traced
Now freely available for evaluation and research purposes.
LynxKite is under active development.
Check out our Roadmap to see what we have planned for future releases.