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What is Big Data?

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What is Big Data?

“Big Data” is one of the commonly used buzz words of our current era, but what does it really mean?

Here’s a quick, simple definition of big data. Big data is data that is too large and complex to be handled by traditional data processing and storage methods. While that’s a quick definition you can use as a heuristic, it would be helpful to have a deeper, more complete understanding of big data. Let’s take a look at some of the concepts that underlie big data, like storage, structure, and processing.

How Big Is Big Data?

It isn’t as simple as saying “any data over the size ‘X ‘is big data”, the environment that the data is being handled in is an extremely important factor in determining what qualifies as big data. The size that data needs to be, in order to be considered big data, is dependant upon the context, or the task the data is being used in. Two datasets of vastly different sizes can be considered “big data” in different contexts.

To be more concrete, if you try to send a 200-megabyte file as an email attachment, you would not be able to do so. In this context, the 200-megabyte file could be considered big data. In contrast, copying a 200-megabyte file to another device within the same LAN may not take any time at all, and in that context, it wouldn’t be regarded as big data.

However, let’s assume that 15 terabytes worth of video need to be pre-processed for use in training computer vision applications. In this case, the video files take up so much space that even a powerful computer would take a long time to process them all, and so the processing would normally be distributed across multiple computers linked together in order to decrease processing time. These 15 terabytes of video data would definitely qualify as big data.

Types Of Big Data Structures

Big data comes in three different categories of structure: un-structured data, semi-structured, and structured data.

Unstructured data is data that possesses no definable structure, meaning the data is essentially just in one large pool. Examples of unstructured data would be a database full of unlabeled images.

Semi-structured data is data that doesn’t have a formal structure, but does exist within a loose structure. For example, email data might count as semi-structured data, because you could refer to the data contained in individual emails, but formal data patterns have not been established.

Structured data is data that has a formal structure, with data points categorized by different features. One example of structured data is an excel spreadsheet containing contact information like names, emails, phone numbers, and websites.

If you would like to read more about the differences in these data types, check the link here.

Metrics For Assessing Big Data

Big data can be analyzed in terms of three different metrics: volume, velocity, and variety.

Volume refers to the size of the data. The average size of datasets is often increasing. For example, the largest hard drive in 2006 was a 750 GB hard drive. In contrast, Facebook is thought to generate over 500 terabytes of data in a day and the largest consumer hard drive available today is a 16 terabyte hard drive. What quantifies as big data in one era may not be big data in another. More data is generated today because more and more of the objects surrounding us are equipped with sensors, cameras, microphones, and other data collection devices.

Velocity refers to how fast data is moving, or to put that another way, how much data is generated within a given period of time. Social media streams generate hundreds of thousands of posts and comments every minute, while your own email inbox will probably have much less activity. Big data streams are streams that often handle hundreds of thousands or millions of events in more or less real-time. Examples of these data streams are online gaming platforms and high-frequency stock trading algorithms.

Variety refers to the different types of data contained within the dataset. Data can be made up of many different formats, like audio, video, text, photos, or serial numbers. In general, traditional databases are formatted to handle one, or just a couple, types of data. To put that another way, traditional databases are structured to hold data that is fairly homogeneous and of a consistent, predictable structure. As applications become more diverse, full of different features, and used by more people, databases have had to evolve to store more types of data. Unstructured databases are ideal for holding big data, as they can hold multiple data types that aren’t related to each other.

Methods Of Handling Big Data

There are a number of different platforms and tools designed to facilitate the analysis of big data. Big data pools need to be analyzed to extract meaningful patterns from the data, a task that can prove quite challenging with traditional data analysis tools. In response to the need for tools to analyze large volumes of data, a variety of companies have created big data analysis tools. Big data analysis tools include systems like ZOHO Analytics, Cloudera, and Microsoft BI.

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Blogger and programmer with specialties in Machine Learning and Deep Learning topics. Daniel hopes to help others use the power of AI for social good.