GQLAlchemy, an Object Graph Mapper for Python, has introduced new functionalities that simplify working with data streams and database triggers in Memgraph, eliminating the need for direct management using the Cypher query language. The library allows Python developers to connect to data streams from Kafka, Pulsar, or Redpanda and execute graph analytics seamlessly. Additionally, GQLAlchemy facilitates the creation and management of database triggers that respond to CREATE, UPDATE, and DELETE operations by executing custom Cypher queries or Python procedures. These features enhance the ease of integrating continuous data updates and notifications within graph analytics workflows, offering flexibility in handling dynamic datasets. The tutorial encourages users to explore further options by implementing custom procedures and highlights the potential of GQLAlchemy to streamline complex data operations in Python environments.