Cray's YarcData claims early success for graph database appliance

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1 Cray's YarcData claims early success for graph database appliance Analyst: Matt Aslett 10 Aug, 2012 The launch of YarcData earlier this year gave supercomputing specialist Cray a graph database appliance with which to target enterprise interest in 'big data' technologies. The YarcData division appears to be off to a good start, and has outlined its plans to increase adoption of its urika appliance over the next few years. The 451 Take The market for graph database and analytics is in its infancy. Add that to the fact that YarcData has a very different model from its competitors, and is taking Cray into new territory, and we believe that a double-digit customer count is a good start. We are also impressed with the company's modest plans for growth, which suggest a realistic assessment of its ability to make progress in what is still an emerging market, as well as its product strategy, which combines the advantages of a pre-integrated hardware appliance with the flexibility of a subscription model. Context We previously covered the launch of YarcData and its urika graph database appliance in the wider context of the broadening of parent company Cray's product portfolio. It is worth taking a closer look at YarcData and its urika graph appliance, especially since it is already seeing early enterprise adoption. First, a reminder of the basics: YarcData is a new division of Cray, established in February to target enterprise interest in big data by developing and marketing its urika graph database appliance. YarcData is led by president Arvind Parthasarathi, who was previously senior vice Copyright The 451 Group 1

2 president and general manager of Informatica's master data management business unit. YarcData is focused specifically on the needs of enterprise customers, in contrast to its parent company's focus on government, energy and science, although it owes its existence to a Cray customer (and US government agency) that began working with Cray's knowledge management practice four years ago to solve a class of problems that it was not able to address, despite being an early adopter of big-data technologies such as MapReduce. The solution to those problems turned out to be graph analytics and graph database technology, and eventually led to the formation of YarcData and the creation of urika. Launched in late February, urika is a graph database appliance that combines an optimized hardware and custom processor with a resource description framework (RDF) data store and SPARQL query engine designed to support the development of graph analytics applications and complement existing analytics technologies by providing a platform to perform exploratory analytics to identify relationships between entities. URiKA is designed to scale shared global memory up to 512TB and I/O up to 350TB per hour, and is based on its Threadstorm massively multithreaded processor designed to support up to 128 simultaneous threads per processor (Threadstorm is based on concepts first deployed in the Cray MTA architecture). This multi-processing capability is required to support the level of processing needed for graph analysis, which involves processing nodes, properties and edges. While nodes represent entities, and properties represent the information related to those entities, edges are the relationships that connect nodes to other nodes, or nodes to properties. Users can navigate? or traverse? the resulting graph by nodes, properties or edges. YarcData has also chosen to make use of existing standards in its software: RDF is a family of W3C standards that form a significant part of its semantic Web project, while SPARQL is an RDF query language and a W3C standard. URiKA users see a SPARQL interface that can be used to write applications that query data stored in RDF format. YarcData reports that it has already gathered a double-digit number of customers (it isn't being more precise than that) for urika, which is a decent number, since adoption of graph analysis is still in its infancy. Given that, YarcData is under no illusions that mainstream adopters will take some persuading that graph analysis is something that they have a need for; it is hiring domain experts in a number of areas to build demos and walk would-be adopters through the use cases. The company reports interest from financial services, life and health sciences, and the intelligence industries, with example applications including drug discovery and trading-strategy development. One of the reasons for the rapid adoption might be the model chosen by the company for licensing the product? although it is a hardware appliance, it is being rented to users via a subscription Copyright The 451 Group 2

3 model that reduces the capital budget required to get up and running. The company is taking a long-term view in terms of its expectations for growth, however. Year one, 2012, is all about finding key use cases and landing reference customers. Year two will be about repeating those early successes and growing to five customers in each of its core target markets, and year three will be about ramping up for mainstream adoption. That said, the company is already investing in growing its employee base. YarcData isn't disclosing the precise numbers, but we understand the team has effectively doubled since its formation. Competition The primary competition for YarcData is going to come from the existing graph database and graph analytics providers. Neo Technology is probably the best-known graph database company, having built a business around its open source Neo4j project, while other graph database vendors include Objectivity Inc's InfiniteGraph and Sparsity Technologies, as well as NuvolaBase's managed service based on the OrientDB project. Other graph databases include Kobrix Software's HyperGraphDB, which is targeted mainly at semantic Web and artificial-intelligence projects; Franz's AllegroGraph RDFStore, also targeted at the semantic Web; and InfoGrid from NetMesh, another open source project. Earlier this year VMware launched the open source AffinityDB project to cover a range of use cases, including graph analysis. In addition, there are a number of graph processing engines available, including Microsoft's Trinity distributed graph database research project, Google's Pregel graph processing platform and the Apache Giraph Pregel implementation. The major relational database providers have largely ignored the potential challenge of graph databases to date; however, with version 10 of its DB2 database and InfoSphere Warehouse technology, IBM added support for the storage and management of graph triples, as well as the SPARQL query language. While it does not want for competitors, none of the other players have adopted an appliance-based approach, and YarcData believes that by optimizing and pre-integrating the hardware and software, urika is four to five times faster than other approaches to graph analytics. SWOT Analysis Strengths Weaknesses If there is one thing that Cray knows about, it is the demand of high-performance computing, and we see the potential for this expertise to translate into graph analysis. Opportunities Threats The company is not known as a supplier to mainstream enterprises, and YarcData will have to establish a reputation separate from Cray's supercomputing credentials. Copyright The 451 Group 3

4 We are seeing increasing interest in graph database technologies (albeit from niche areas at this stage) as a platform for new analytic applications that cannot be supported by traditional data models. IBM is the first of the major database vendors to add support for graph storage to its relational database. If graph analysis takes off, expect the others to follow suit. Copyright The 451 Group 4

5 Reproduced by permission of The 451 Group; This report was originally published within 451 Research s Market Insight Service. For additional information on 451 Research or to apply for trial access, go to: Copyright The 451 Group 5