The Race to Reduce Latency in Big Data
Latency is the enemy of a streamlined, effective system in the world of big data. Every IT professional knows that a delay between a signal and a response is wasted time. Data that arrives continuously needs to be processed immediately. The three big motivations for having a latency-free system are: wanting an analysis using real-time data, wanting to adjust responses based on real-time data, and wanting to back up and preserve data. Latency is a huge deal in big data analytics because millions of events can happen within a single minute. Having the ability to track each of those events plays a crucial role in utilizing data to its fullest capacity. In fact, some data may actually be irrelevant if it is left dormant for even just a few hours. This is especially true when it involves data that is collected in order to create an intuitive response to real-time user interaction. Latency is also a big deal when it comes to keeping a network safe. Being alerted to an inconsistency in data won’t be very beneficial if the alert is processed minutes or hours after the fact. The bottom line is that latency isn’t just the buzzword of the day in the tech world. Latency is quickly becoming the difference between relevant data collection systems and irrelevant blueprints. Take a moment to learn why every event counts in a digital world where millions of events can happen in a single minute.
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