Struggling to choose between JanusGraph and ArangoDB? Both products offer unique advantages, making it a tough decision.
JanusGraph is a Development solution with tags like graph, database, distributed, scalable, cassandra, hbase.
It boasts features such as Distributed graph database, Supports various storage backends like Cassandra, HBase, etc, Scalable to handle large graphs, Support for complex traversals and graph algorithms, Native integration with Spark and TinkerPop Gremlin, Support for geo, numeric range and full-text search, ACID and serializable transactions, Multi-datacenter high availability and pros including Highly scalable, Flexible storage backend options, Strong consistency support, Powerful graph querying capabilities, Integrates well with big data stack.
On the other hand, ArangoDB is a Development product tagged with nosql, multimodel, graph, document, search.
Its standout features include Multi-model database supporting documents, graphs and key-value pairs, Flexible data models, High performance, Scalable distributed architecture, Built-in search engine, Native graph database, Joins and transactions across data models, Role-based access control, Encryption, Backups and replication, and it shines with pros like Supports multiple data models in one database, Good performance for reads and writes, Scales horizontally, Has a free open source edition, Query language AQL is similar to SQL, Can be deployed on-prem or in the cloud.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
JanusGraph is a scalable, distributed graph database optimized for storing and querying large graphs. It is an open source project under the Linux Foundation and supports storage backends like Cassandra and HBase.
ArangoDB is a native multi-model database system that supports graph, document, and search models. It is designed for scalability, high performance, and ease of use.