Neo4j Labs is a collection of the latest innovations in graph technology. These projects are designed and developed by the Neo4j team as a way to test functionality and extensions of our product offerings. A project typically either graduates to being maintained as a formal Neo4j project or is deprecated with source made available publicly.
These Labs are supported via the online community. They are actively developed and maintained, but we don’t provide any SLAs or guarantees around backwards compatibility and deprecation, see the FAQ below.
As the most comprehensive developer toolkit for Neo4j, the APOC library provides a wide range of procedures and functions that make your life as a Neo4j user easier. APOC includes data integration, graph refactoring, data conversion, operational functionality and more.
Having an easy way of loading data from relational databases into Neo4j is one of the first steps many users take. The Neo4j-ETL Tool makes this easy by inferring a graph model from the relational meta model that you can then adapt to fit your needs. Given that transformation, this tool also handles the actual import for you.
GraphQL has become a comprehensive stack for API development and consolidation. Our GRANDstack and Neo4j-GraphQL-js offerings combine the most common tools and frameworks: GraphQL, React, Apollo and Neo4j Database.
The Halin Monitoring App allows you to monitor your Neo4j deployment and identify bottlenecks or incorrect configurations, with insights into currently running queries and workloads. The app also provides access to metrics and logs.
Neo4j-Helm is a tool for configuring and deploying Neo4j instances on Kubernetes. By using the Helm package manager for Kubernetes, it makes it simple to specify advanced configurations of Neo4j, both standalone and cluster, and run them with Kubernetes across many cloud platforms.
Neosemantics integrates RDF and Linked Data with Neo4j. It allows to import a wide variety of RDF formats and to expose Neo4j property graphs as Linked Data. Ontology and Inference are also partially supported.
Neovis.js, a graph visualization toolkit for the web
The following projects were successfully developed, incubated, and validated within Neo4j Labs and have graduated to official and supported Neo4j products ready for production use at scale.
To enable larg scale graph analytics and support machine learning pipelines we developed the Neo4j Graph Algorithms library, which covers many widely used algorithms. The library offers highly parallelized implementations that work well with large scale graphs. It graduated and is now available as part of the Neo4j Graph Platform as Graph Data Science Library. Educational content is available from Neo4j Labs.
Streaming event data is an integral part of most modern data architectures. With the Neo4j Connector for Apache Kafka you can integrate Neo4j both as a sink or source into your setup. The integration is available as a Kafka Connect plugin and Neo4j Server extension. It is officially supported as an Ecosystem Connector.
Data processing in Apache Spark is commonplace and available on all cloud platforms. With the Neo4j Connector for Apache Spark you can read from and write to Neo4j from your Spark Jobs. Built on the new DataSource API it supports usage from Python, R and Scala. It is officially supported as an Ecosystem Connector.
Current Neo4j Labs projects are being actively worked on by our engineers, and may be rough around the edges, with changing APIs, as they push the edge of the envelope. Therefore, we cannot provide official commercial support for these projects or guarantee longevity. However, some Neo4j customers and users still love the functionality of these projects and choose to continue using them in production environments.
We welcome contributions for those labs which are open source projects. You’ll find links to GitHub repositories - feel free to submit PRs. We’ve also created a discussion category for Labs on community.neo4j.com
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