* Import data (megabytes to terabytes) from a variety of sources. Work directly with traditional data sources (CSV, JSON, ORC, Parquet files — local or Hadoop, JDBC, Hive, etc.) or from a graph DB like Neo4j.

* Turn data easily into graphs.

* Use algorithms from a large library of graph operations, including graph AI operations.

* Put together complex data processing pipelines where you can combine graph operations, classical data analysis operations and machine learning.

* Discover graphs and interpret algorithm results interactively, at any stage or step of the calculations, easily experimenting with different approaches and tuning parameters.

* Seamlessly combine the benefits of a friendly “no code” GUI as well as coding via powerful Python integration (code embedding, Python API, code generation).

* Accelerate adoption of graph data modelling and analytics in your organization by creating your own tutorials or wizards that allows less experienced people to contribute and learn.