3 steps to making Hadoop data aerodynamic
Comparing Hadoop big data analytics to an aerodynamic vehicle produces a fairly apt parallel – both are modern concepts that harness lots of information and figure out how to concentrate it for optimal results. Big data can produce a lot of figurative weight and drag if insights aren't directed with the right focus, and the friction caused by retrieval lags can torpedo organizational growth. Here are three ways to make big data analytics soar.
1) Conquer the air
This step might sound silly, but it comes in the spirit of believing that the sheer amount of available information can be conquered. Big data arrives constantly and from various sources, continuously regenerating and offering new perspectives. According to DotNetNuke CEO Navin Nagiah, data streams will continue to get more crowded, but that doesn't have to mean analytics efforts must become cloudier. On the contrary, having the data and being able to use it will be paramount to success.
"In the business world, it is the company that has the data that has the power," wrote Nagiah.
2) Concentrate the power
Big data possession is important, but analytical insight will plateau if companies aren't using the right tools. Whether big data analytics are directed toward strengthening customer relationships, synchronizing business operations or innovating solutions, organizations will benefit from information immersion. The inherent capabilities that Hadoop architecture offers analytics users are a good starting point because they're naturally geared toward data accrual.
3) Put big data in motion
The third step is the implementation of Apache Hadoop, the software that makes data effective for complex insights and ones made in real time. A recent TechRepublic report looked at data in motion, the kind of real-time analysis that offers significant advantages for its users. Components like Hadoop Hbase and Hive enable instantaneous, integrated insights by allowing ad hoc users to interface directly with databases, producing solutions that can be seamlessly applied without slowing down incoming data streams.
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