AI, ML, NLP There are a lot of opportunities of using graph databases with ML workflows. From providing additional features to using graph algorithms for unsupervised learning. Graphs can also represent neural networks, and approaches like graph2vec and deepgl offer interesting insights via embedding extraction and clustering. Other 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 Stream Processing 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. BI & Visualization There is always a strong need to visualize and represent data of any database. That’s not different in a graph database. Luckily there are a lot of options for integrating with existing BI solutions but also dedicated graph visualizations availble. Orchestration Orchestration frameworks allow you to efficiently allocated and provide resources to applications and services. You can run Neo4j both on Kubernetes environments e.g. via Helm as well as on Mesos via Marathon. 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 :) 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. GeoSpatial 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!).