Seeing Graphs in Smart Data Lakes

Seeing Graphs in Smart Data Lakes

Seeing Graphs in Smart Data Lakes

Data lakes are synonymous with Hadoop to many people grappling with the promise and the peril of big data. That’s not surprising, considering Hadoop’s unparalleled capability to gobble up petabytes of messy data. But for Barry Zane and other folks at Cambridge Semantics, data lakes are taking on a decidedly graph-like appearance.

Cambridge Semantics, which acquired Zane’s latest startup SPARQL City earlier this year, is beginning to talk about its concept of the smart data lake. The data lake concept is a well-worn one by now. The “smart” part, you may have guessed, owes to the semantic aspect of how the data is stored, how it’s connected to other data in the lake, and the way it impacts how people can extract meaningful information from it.

To Zane’s way of thinking, those who can get the most insights with the least amount of effort have an advantage. Of course, this has always been the case. But the telling part is the fact that Zane—who was founder and CTO of ParAccel (acquired by Actian) and a co-founder and VP of architecture at Netezza (acquired by IBM)–sees graph databases and graph analytic technology as the best way to get there for at least the next 10 years.

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“We strongly believe that this is an extremely effective approach, a future-proof approach,” Zane tells Datanami. “Just as Hadoop basically came of maturity because relational just wasn’t able to work with a certain class of question and wasn’t able to work at a certain scale, we pursue those classes of questions and scale using the graph standards, at an incredible cost and performance advantage, as compared to hiring programmers for every question and analytic you want to perform.”

Zane, who is Cambridge Semantics vice president of engineering, sees graph databases—such as the Anzo Graph Query Engine–as a natural evolution from relational databases, which he says have developed some pretty powerful analytic capabilities themselves over the past 40 years.

“Without a doubt what we’re doing is educated by learning from Netezza, educated from learning from ParAccel.  So I really see it a just an evolution,” Zane says. “The difference is you’re able to ask more interesting question of your data. You’re able to find relationships that are otherwise nearly to impossible to find.”

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The core problem with relational database technologies—even the massively parallel processing (MPP) technologies that he championed at ParAccel (which powers Amazon’s Redshift data warehousing service) and Netezza (which IBM has renamed into something that nobody can ever remember)—is the ease at which advanced analytics can be performed, and the length of time it takes to get answers back.

“Being a longtime relational guy, one of the great things about the relational database is that you don’t need to be programmer. You’re able to work with the database through either a set of application layer tools or in the SQL language,” he says.

“The best way to think of SPARQL and RDF is that they’re just the next evolution of relational database SQL,” he continues. “That’s the way I think about it, and that’s what got me excited because you can have people who are not super high trained programmers be able to post queries of the data in a matter of minutes or hours and get back response in a matter of seconds or minutes, as opposed to hiring very highly trained and expensive programmers for any given query.

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