The Race to Reduce Latency in Big Data

Take a moment to learn why every event counts in a digital world where millions of events can happen in a single minute.

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.

The Difference Between Batch Processing and Stream Processing in Latency Performance

Batch processing and stream processing are two options enterprises have to choose from when designing systems for big data analytics. The mechanics of both are pretty straightforward. Batch processing is a method that periodically collects, processes and stores data based on predetermined intervals or triggers. Stream processing, on the other hand, does not wait for a trigger to process a data flow. This method processes a data event as soon as it is received. The true difference between these two methods comes down to lag times. Stream processing offers much more of a real-time monitoring system than batch processing does. Both of these methods can be superior depending on what your intention for collecting data is. However, stream processing is considered superior if the goal is to make latency as nonexistent as possible.

Low Latency Means Everything for Some Industries

There is simply no getting around the fact that every industry requires systems with low latency. However, some industries are more reliant on the notion of achieving low latency than others. Banking and financial organizations are particularly dependent on systems that are free from lag times. Big data analytics allow banking and financial organizations to get real-time analysis of everything from customer habits to market trends and risks. In addition, quick analysis can make a big difference when it comes to protecting the integrity of data and keeping customer information private. Consumer-based companies also have a big stake in the game when it comes to getting the lowest latency levels possible. Any enterprise that markets products or does commerce can benefit from getting real-time glimpses into the behaviors of their customers. These enterprises often use data that’s been collected to create predictive analytics based on the habits and behaviors of customers. In all of these industries, the ability reduce latency in data analytics can have significant returns on business growth.

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Author: Alex Summers

This entry was posted on Thursday, July 21st, 2016 at 6:30 pm and modified by WebMaster View on Friday, July 22nd, 2016 at 10:39 am. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.

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