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8 big trends in big data analytics

Robert L. Mitchell | Oct. 24, 2014
Big data technologies and practices are moving quickly. Here's what you need to know to stay ahead of the game.

But there's a lot of hype around HTAP, and businesses have been overusing it, Beyer says. For systems where the user needs to see the same data in the same way many times during the day -- and there's no significant change in the data -- in-memory is a waste of money.

And while you can perform analytics faster with HTAP, all of the transactions must reside within the same database. The problem, says Beyer, is that most analytics efforts today are about putting transactions from many different systems together. "Just putting it all on one database goes back to this disproven belief that if you want to use HTAP for all of your analytics, it requires all of your transactions to be in one place," he says. "You still have to integrate diverse data."

Moreover, bringing in an in-memory database means there's another product to manage, secure, and figure out how to integrate and scale.

For Intuit, the use of Spark has taken away some of the urge to embrace in-memory databases. "If we can solve 70% of our use cases with Spark infrastructure and an in-memory system could solve 100%, we'll go with the 70% in our analytic cloud," Loconzolo says. "So we will prototype, see if it's ready and pause on in-memory systems internally right now."

Staying one step ahead
With so many emerging trends around big data and analytics, IT organizations need to create conditions that will allow analysts and data scientists to experiment. "You need a way to evaluate, prototype and eventually integrate some of these technologies into the business," says Curran.

"IT managers and implementers cannot use lack of maturity as an excuse to halt experimentation," says Beyer. Initially, only a few people -- the most skilled analysts and data scientists -- need to experiment. Then those advanced users and IT should jointly determine when to deliver new resources to the rest of the organization. And IT shouldn't necessarily rein in analysts who want to move ahead full-throttle. Rather, Beyer says, IT needs to work with analysts to "put a variable-speed throttle on these new high-powered tools."


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