Ecosystem & Integrations
GeoSpatial & Location-Based Data Neo4j 3.0 introduced the spatial point and distance functions in Cypher. Since Neo4j 3.4, spatial points can be stored as properties, indexed in the schema index, and queried at high performance using both range (and bounding box) as well as distance searches directly within Cypher (and we added 3D too!). Graph Intelligence for Microsoft Fabric Graph Intelligence for Microsoft Fabric enables Data Scientists and Data Analysts to create graphs from tabular data stored in OneLake and then explore, query and run graph algorithms all within the Microsoft Fabric console.** Databases & ETL Neo4j can integrate with a number of relational and non-relational databases. Either with dedicated integrations and #neo4j-graph-platform:etl tools or via procedure libraries like #neo4j-graph-platform:procedures-apoc Streaming (Kafka, Spark, Flink) Stream processing is used for both service integration and large scale data processing in Apache Kafka, Spark and, Flink. Neo4j integrates with all these libraries. Orchestration & Kubernetes Orchestration frameworks allow you to efficiently allocated and provide resources to applications and services. You can run Neo4j on Kubernetes environments e.g. via Helm. Docker You can easily run any Neo4j version from docker. It allows to set up enterprise clusters and configure the images. The Neo4j sandbox and several of the Neo4j Cloud offerings run on top of the official Docker image. Graph Analytics for Snowflake Graph Analytics for Snowflake enables Data Scientists to read data from Snowflake tables, project them into memory inside Snowpark Container Services, run graph algorithms and write the results back to Snowflake tables. Linked Data, RDF, Ontology Welcome RDF / Linked Data / Ontology practitioners (or just curious people). If you're asking yourself how do any of these technologies relate/integrate with Neo4j, you're in the right place :)
