A Connected ETL and graph modelling interface provides a seamless workflow for creating and updating the knowledge graph. Rather than exporting data from the ETL tool and importing it into the graph modelling interface, users can work with the data in real-time and make changes on the fly. This reduces the risk of errors and ensures that the graph model is always up-to-date with the latest data.

Users can ensure data consistency between modelling and ETL processes. This is especially important when dealing with large and complex data sets, where inconsistencies can quickly lead to errors in the graph model. With a connected interface, users can easily track the data mapping from the ETL process to the graph model and ensure that the data is being correctly transformed and loaded into the graph.

The connected process approach can also improve productivity by reducing the time and effort required to create and update a graph model. With no need to manually map data between the ETL and graph modelling tool, users can focus on higher-level tasks such as defining the schema and ontology of the graph. This can save a significant amount of time and reduce the risk of errors in the modelling process.

Users can experiment with different data mapping and transformation strategies, and see the impact of these changes in real-time on the graph model. This enables users to fine-tune the graph model to better represent the domain, and derive more meaningful insights from the data.