Actian Brings Graph Analysis To Big Data - InformationWeek

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11/4/2014
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Actian Brings Graph Analysis To Big Data

Actian adds in-memory, parallel processing engine for graph analysis based on SPARQL. Can it compete with popular alternatives?

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Actian on Tuesday announced that it's adding a graph-analysis engine to its big data portfolio, which already includes a SQL-on-Hadoop offering as well as several relational databases and data-integration software.

Graph analysis is applied to uncover networked relationships among people, places, things, and entities. It's at the core of Facebook's ability to uncover relationships, and in business it's used to uncover customer relationships in retail and telecommunications; governance and compliance relationships and risks in financial services industries; and claim and readmission risk analysis in healthcare.

Actian's new graph analysis option is SPARQLverse, an in-memory engine that employs the World Wide Web Consortium's (W3C) SPARQL Protocol and RDF Query Language standard. SPARQLverse was developed by SPARQL City, a separate software engineering company that Actian has invested in but that has limited sales and marketing capacity of its own. Actian will act as a SPARQLverse reseller.

[Want to hear about a different approach to delivering multiple data-analysis options? Read Teradata Brings Graph Analysis To SQL.]

There's no shortage of popular products that have implemented SPARQL, including popular databases from IBM and Oracle and graph-analysis-specific products including Neo4J, Cray's Urika-GD, and Sqrrl (built on Apache Accumulo). Other popular or up-and-coming graph-analysis options that don't use SPARQL include Apache Spark GraphX, Apache Giraph, the Titan distributed graph database, and Teradata's SQL-GR engine.

So why use SPARQLverse when other options offer either wide adoption and popularity or, in the case of Apache Spark and Teradata, multiple analysis options on a single analytic platform?

Products that don't use SPARQL "have no standard, declarative, SQL-like language," according to Barry Zane, SPARQL City's CEO, while other products "can't match SPARQLverse in-memory, parallel-processing performance," he says.

Zane points to SPARQL City benchmark tests that show that its product runs 10 times faster on 100 times the data volume handled by "other graph solutions." The list of other products includes less-well-known products including the Java engine ARQ on top of open source Jena, the Redland RDF Processor, Java-based Sesame, and OpenLink Virtuoso.

SPARQLverse can run on top of Hadoop and also query Hadoop as well as relational and NoSQL sources, but it's a separate engine from the Actian Analytics Platform Hadoop SQL Edition. Actian describes its collection of products as an analytics platform, but it has yet to integrate separate products in terms of administration or analysis -- a Spark vision and strength that companies including Teradata have tried to match.

SPARQLverse can also run as a stand-alone, distributed cluster on HDFS. SPARQL City is working with a big investment bank as a beta customer, according to Zane, but he said he could not disclose the name of the company.

SPARQL City is developing applications including insider-trading and derivatives reporting. The insider-trading app looks across trading data and geospatial information to uncover non-obvious connections among traders. The derivatives-reporting app looks at product and geospatial data to accurately identify the source of derivatives products worldwide.

Actian says it's considering an even wider scope of graph-analysis applications, including fraud detection, DNA research, customer-influence analysis, and Internet-of-Things log analysis.

What will you use for your big data platform? A high-scale relational database? NoSQL database? Hadoop? Event-processing technology? One size doesn't fit all. Here's how to decide. Get the new Pick Your Platform For Big Data issue of InformationWeek Tech Digest today. (Free registration required.)

Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of ... View Full Bio

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D. Henschen
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D. Henschen,
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11/4/2014 | 3:18:07 PM
If I could invest in tech...
To avoid conflicts of interest, our company forbids reporters from investing directly in tech companies that they cover or even get close to covering. As near as I can get is buying an ETF or mutual fund that has shares of ALL tech securities. Were that not the case, I'd put my money on companies seriously backing Apache Spark, which offers a compelling combination of analysis approaches including machine learning, SQL, graph, streaming data analysis and more.

Now the caveats: The Spark one-stop-shop for analytics concept is being widely copied, and it's also a threat to many vendors who know their best chance of making money in the long term CAN'T rest on providing a commoditized storage platform (like Hadoop). It's also true that it's not easy being best at many different forms of a analysis, so look for best-of-breed threats in individual analysis domains. I think 2015 will be a crucial year for Databricks/Apache Spark to prove that it has a highly usable, highly versatile analytics platform that can support efficient analysis workflows and emedding of decision- and prediction-services into applications.
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