Streaming and Trigger Support With GQLAlchemy

GQLAlchemy enhances the functionality of Memgraph by allowing Python developers to programmatically manage data streams and database triggers, eliminating the need to directly use Cypher query language for these tasks. This integration facilitates connecting to data streams from platforms like Kafka, Pulsar, and Redpanda, and running graph analytics on the data, while also simplifying the creation and management of database triggers for operations such as CREATE, UPDATE, and DELETE. Triggers enable custom notifications and the execution of graph algorithms post-data updates, which can be extended with Python-written query modules for further data analysis or integration with external systems. This approach streamlines workflows for Python developers in the graph database environment, offering a more accessible and flexible way to handle dynamic data interactions and analytics through GQLAlchemy's interface.