How To Do Real-Time Analytics The Right Way

By Jonathan Buckley, Senior VP of Marketing, at Qubole

Many businesses are pushing their IT departments to not just adopt Big Data solutions, but to enact real-time analytics operations. Much has been made about the benefits that can come from using Big Data analytics, with real-time analytics being perhaps the most advanced form of this technology. While the benefits are certainly genuine, real-time analytics can be a complicated topic, with many companies doing it incorrectly or for the wrong reasons. With the demand for real-time analytics seemingly growing daily among numerous different types of industries, businesses need to understand the difference between doing it the right away and doing it poorly. Noting this distinction will often lead to success on the Big Data analytics front.

It’s clear that organizations want to use real-time analytics more and more. With the increased use of cloud computing and more attention being paid to Big Data tools like Hadoop and Apache Spark, the time is right to adopt real-time analytics. But it’s first important to understand that real-time analytics is often misinterpreted. What many businesses think qualifies as real-time analytics might actually be near real-time or even batch. These three items are definitely related and carry many of the same benefits, but they have different use cases for which they are best suited. In the case of real-time analytics, the goal is to use live data rather than historical data. In some cases, even data that’s a few hours or minutes old might lead to the wrong results. In such instances, real-time analytics should be the preferred choice of companies.

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