{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/3"}],"enrichment":{"capability":"Integrates Neo4j graph database operations into Apache Airflow workflows, providing operators and hooks to query, write, and manage graph data as part of DAG tasks.","skillfed_tags":["airflow-provider","graph-database","workflow-orchestration"],"use_cases":["Schedule regular Cypher queries to extract or analyze graph data and feed results into downstream Airflow tasks.","Build ETL pipelines that load data from external sources into Neo4j as part of a coordinated workflow.","Orchestrate graph analytics jobs that depend on other data processing steps in a single DAG.","Monitor Neo4j database health or query performance as part of a larger Airflow monitoring workflow.","Synchronize graph data between Neo4j and other data systems (data lakes, warehouses) in a scheduled pipeline."],"what_it_does":"This is an Apache Airflow provider package that bridges Airflow's workflow orchestration engine with Neo4j graph databases. It supplies operators and hooks that allow you to define Airflow DAG tasks that execute Cypher queries, manage graph data, and integrate Neo4j operations into larger data pipelines. The package is maintained as part of the official Apache Airflow ecosystem and follows Airflow's provider plugin architecture.\n\nYou install it alongside an existing Airflow instance (version 2.11.0 or later) and a Neo4j driver (5.20.0 or later). Once installed, you can use its operators to construct tasks that read from, write to, or query your Neo4j instance as part of scheduled or triggered workflows. It is production-stable and actively maintained, with support for Python 3.10 through 3.14.","worth_installing":"Yes, if you run Apache Airflow and need to integrate Neo4j operations into your workflows. The package is production-stable, actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is low. The only prerequisite is an existing Airflow instance and a Neo4j connection configured in Airflow's connection store."},"id":"apache-airflow-providers-neo4j","links":{"html":"https://skillfed.io/packages/apache-airflow-providers-neo4j","md":"https://skillfed.io/packages/apache-airflow-providers-neo4j.md","pypi":"https://pypi.org/project/apache-airflow-providers-neo4j/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-08","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"apache-airflow-providers-neo4j","python_support":"supports_current","summary":"Provider package apache-airflow-providers-neo4j for Apache Airflow"},"popularity":{"monthly_downloads":366845,"position":7202,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.12.1"}