05 Nov What’s New in TigerGraph: Powering the Fastest Performance For Graph Analytics
Graph databases and analytics have crossed the chasm – and the data supports it. Take, for example, how graph databases are the fastest growing category in all of data management (according to DB-Engines.com). Survey data further shows that more than 40 percent of organizations currently use graph technology. Further, more than 54 percent who don’t use graph technology say they are looking into it.
More and more Proofs of Concept are becoming large, operational and analytical deployments. As they do, organizations share a pressing concern: the need for scalability. In fact, a study by the University of Ottawa calls out the ability to process very large graphs efficiently as the biggest limitation of existing graph software today. This makes sense as the amount of data increases with production deployments, to support more sophisticated graph analytics.
The ability to distribute graph over multiple machines or scale out is a key factor for scalability, along with the ability to scale up each machine with additional CPUs and memory. Legacy graph 1.0 databases such as Neo4j and graph 2.0 databases such as Amazon Neptune are unable to distribute graph among multiple machines to scale out.
To achieve maximum value, organizations also need a solution that supports two distinct sets of use cases: OLAP and OLTP with a unified operational graph platform that provides deep link analytics capabilities. First and second-generation solutions like Neo4j and Amazon Neptune are unable to support OLAP and OLTP with a single distributed graph.
Meanwhile the cloud has revealed itself as the logical choice to support graph deployments for multiple use cases. Users are turning to cloud platforms such as Amazon Web Services and Microsoft Azure as they seek out comprehensive and scalable graph solutions offering reasonable costs of ownership. Enterprises need the ability to easily tap into the cloud in order to scale out to robust production deployments.
The latest release of TigerGraph addresses these needs and pain points by building upon its MPP native architecture to offer the following:
Frictionless integration, with popular databases and data storage systems including RDBMS, Kafka, Amazon S3, HDFS, and Spark (coming soon). A new TigerGraph EcoSys GitHub repository will now host open source connectors to TigerGraph as they roll out. Users can fork and modify to customize for their own usageCheck out the initial offerings available now at: https://github.com/tigergraph/ecosys/tree/master/etl.
Expanded deployment options, enabling users to implement a performant and scalable graph as they wish, in a cloud-neutral environment. Enjoy one-click install for TigerGraph in major cloud marketplaces – such as Amazon AWS Marketplace and Microsoft Azure – helping customers control where their data is stored to prevent cloud vendor lock-in. TigerGraph also offers new support for container technology in your development and QA environment for easy portability across on-premise and cloud environments.
A graph algorithm library, containing efficient GSQL implementations of popular graph analytics functions such as PageRank, Shortest Path, Connected Components and Community Detection. TigerGraph’s high-performance library offers a user-extensible set of GSQL queries. This uniquely contrasts with other graph algorithm libraries available on the market, like Neo4j’s graph algorithm library – which lacks flexibility by only offering a function call without the ability to modify parameters or add, remove or edit steps.
An eBook for graph developers titled “Native Parallel Graph: The Next Generation of Graph Database for Real-Time Deep Link Analytics.” The ebook offers best practices distilled from graph deployments at the most innovative companies – across a range of industries.
Further, a new industry benchmark report demonstrates how TigerGraph is more than 40x faster than Neo4j and more than 57x faster than Amazon Neptune. TigerGraph’s technical breakthrough represents the next stage in the graph database evolution, as we recently won “Most Disruptive Startup” in the Strata Data Awards.
Since our launch just a year ago, TigerGraph has seen overwhelming adoption and massive success enabling some of the world’s largest deployments for industries. We look forward to continuing our work in bringing the power of graph analytics to enterprises and government organizations – stay tuned for more developments to come!