The Big Scoop on Effectively Handling Big Data

Data weaves its way in and out of our lives seemingly effortlessly these days, with our phones, tablets, and computers becoming the forefront of an information mega-wave. With all the data available to us at high speeds, the big question lurks in the background: how is all this data moving around so quickly and freely? The answer is multifaceted, and it has to do with a few different types of data and the ways they are handled.

Different Data, Different Processes

The most nebulous and difficult type of big data to deal with is called unstructured data, and it is comprised of all the data attached to social media, sensor systems, and log files. In many ways, this data type is the newest to emerge in recent years, and it requires newer techniques to control. These new techniques include unstructured text analysis, news analytics, and natural language processing, and the engineers looking to ride the exciting wave of the future would do well to learn and understand these new techniques if they wish to handle unstructured data efficiently.

Next comes data that is somewhat structured - or semi-structured, as it is known in the tech industry. Data from emails, call centers, and the burgeoning Internet of Things all count as semi-structured data; the processes used to parse this data are a mix of the new techniques mentioned above and traditional databases, such as metal servers and real-world storage.

Finally, there is streaming data, which is considered to be the cutting edge of big data due to its always-live, always-moving nature. The enhanced connectivity of the Internet of Things helps increase streaming speeds, but there is still a lot of work to be done to master the art of handling streaming data as fast as possible. This means that this field will be an exciting new place for an engineer to work.

Storage and Computing Should Go Hand in Hand

One of the biggest obstacles to data travel speed is the time it takes to send data freely from its source to other devices. So far, the best way to reduce data travel time is to place the origin of the data as close as possible to the devices that will ultimately parse the data in order to transmit it as quickly as possible. There is less latency this way, and when a cluster of computers and devices is located on-site, a close-knit network of machines would be the best way to ensure information travels quickly and freely throughout your workplace.

A Thorough Data Network is the Ideal Data Network

While an efficient network of machines transmitting data from one to the other sounds fantastic, it is often difficult to achieve. This is unfortunate because the best way to get the data moving is to fashion a systemic solution that goes from one end to the other as seamlessly as possible. Such a complete solution frees up kinks in the system and smooths out data transfer processes, but it requires teams that are competent in their areas of expertise and are adept at communication to keep the network open for mass data travel. Engineers often become the crux of these processes thanks to their knowledge, so the future for engineers is bright.

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Joshua Crawford | Managing Director | PROPRIUS