08 Jan Infographic: The Graph Database Landscape
Graph databases are the fastest growing category in all of data management, according to DB-Engines.com. What makes graph databases so popular? Graphs overcome the big and complex data challenges that other databases cannot. With the popularity of graph databases driven by such benefits, there has been an emergence of new players to the market, creating a new landscape of tools and technologies. We have created an infographic to lay out this new graph landscape, including the following categories and key players:
Download the Graph Database Landscape PDF
Real-Time Big Graphs
TigerGraph offers the newest category of graph databases, called the real-time big graph, that is designed to deal with massive data volumes and data creation rates and to provide real-time analytics. Real-time big graphs enable real-time large graph analysis with both 100M+ vertex or edge traversals/sec/server and 100K+ updates/sec/server. To handle big and growing datasets, real-time big graph databases are designed to scale up and scale out well.
Operational Graph Databases
These solutions tend to be native graph stores or built on top of a NoSQL platform. They are focused at transactions (ACID) and operational analytics, with no absolute requirement for indexes.
Vendors include: Titan, JanusGraph, OrientDB and Neo4j
Knowledge Graph / RDF
These graphs are often semantically focused and based on underpinnings (including relational databases). They are ideal for use in operational environments, but have inferencing capabilities and require indexes even in transactional environments.
Vendors include: AllegroGraph, Virtuoso, Blazegraph, Stardog, and GraphDB.
This category encompasses databases designed to support different model types. For example, a common possibility is a three-way option of document store, key value store or RDF/graph store. The advantages of a multi-modal approach are that different types of queries, such as graph queries and key value queries can be run against the same data. The main disadvantage is that the performance cannot match a dedicated and optimized database management system.
Vendors include: Microsoft Azure Cosmos DB, ArangoDB and Sqrrl.
These analytic graphs focus on solving ‘known knowns’ problems (the majority) – where both entities and relationships are known, or on ‘known unknowns’ and even ‘unknown unknowns.
Vendors include: Apache Giraph and Turi (formerly GraphLab, now owned by Apple).