2 Open Source Alternatives To Neo4j

JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. As an open-source project under The Linux Foundation, it boasts contributions from leading tech companies like Expero, Google, IBM, and Amazon.

• Scalability: Efficiently handles graphs with hundreds of billions of vertices and edges.
• Open Source: Fully open-source under the Apache 2 license, with no commercial licenses required.
• Transactional: Supports thousands of concurrent users executing complex graph traversals in real time, with ACID and eventual consistency.
• Data Storage: Compatible with multiple storage backends including Apache Cassandra, Apache HBase, Google Cloud Bigtable, Oracle BerkeleyDB, and ScyllaDB.
• Search: Advanced search capabilities with optional support for Elasticsearch, Apache Solr, and Apache Lucene.
• Analytics: Supports both OLTP and OLAP with Apache Spark integration for global graph analytics.
• TinkerPop Integration: Native support for the Apache TinkerPop™ graph stack, including Gremlin query language, Gremlin Server, and Gremlin Console.
• Adapters: Third-party storage adapters available for Aerospike, DynamoDB, and FoundationDB.
• Visualization: Compatible with various visualization tools like Arcade Analytics, Cytoscape, Gephi, Graphexp, Graphlytic, G.V() - Gremlin IDE, Key Lines by Cambridge Intelligence, Ogma by Linkurious, and Tom Sawyer Perspectives.

JanusGraph is designed to meet the needs of applications requiring large-scale graph data storage and querying capabilities, making it a robust choice for enterprises and researchers alike.

Hypermode is an advanced solution designed to integrate AI-powered search capabilities into your graph database. It enhances the functionality of your graph database by providing intelligent search features that leverage AI technologies for faster and more accurate data retrieval. This makes it ideal for modern applications that require high performance and scalability in managing complex data relationships.

• AI-Powered Search: Utilize artificial intelligence to improve search accuracy and speed within your graph database.
• High Performance: Benefit from a highly performant query engine that ensures low latency and high throughput.
• Scalability: Easily scale your database horizontally to handle increasing data loads and ensure reliability.
• GraphQL and DQL Support: Choose between GraphQL for simplicity and speed or DQL for more complex query patterns.
• Versatile Use Cases: Ideal for recommendation engines, customer 360 implementations, fraud detection, and more.

Hypermode empowers developers and data engineers to build robust, scalable, and efficient applications by adding AI-enhanced search functionalities to their graph databases. It simplifies the process of managing and querying complex data relationships, making it a valuable tool for modern data-driven applications.