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Introduction to Big Data

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Characteristics of Big Data

2:30 with Craig Dennis and Jared Smith

How do you characterize Big Data?

Terms

petabyte -- 2^50 bytes; 1024 terabytes, or a million gigabytes.

The 4V's of Big Data

  • Volume - the scale
  • Velocity - the speed
  • Veracity - the certainty
  • Variety - the diversity

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We know that there might be more than 4 Vs in the world of Big Data, but for those of you getting started in the Industry, these are the most relevant to remember

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    Big data spans a broad range of data sets 0:00
    that are nearly impossible to use without specialized tools and systems. 0:03
    Now, I'd like you to think of big data as not only the data sets that are be 0:06
    processed, but also the infrastructure that's needed to support such analysis. 0:10
    Now this spans from ingesting the data in the back end all the way to displaying 0:14
    visualizations on the front end. 0:18
    Big data plays a part in all of that. 0:19
    Big data is typically characterized by what is known as the four V's. 0:22
    That's volume, velocity, variety, and veracity. 0:26
    Let's take a look at each one of those. 0:30
    The size of the data helps to define whether it can 0:32
    actually be considered big data. 0:35
    You define the volume by the size of the data in gigabytes, 0:37
    terabytes or even petabytes. 0:41
    Now this varies widely across data sets, 0:43
    but usually anything over one gigabyte is considered to be a large volume. 0:44
    The velocity helps you define the challenges and 0:50
    the demands that make growth and development difficult. 0:52
    This is often defined by the problem space. 0:55
    Now for example, one problem space you might encounter is that you're doing 0:57
    search querying in real time, so you want to process data extremely quickly. 1:01
    This is typically called streaming. 1:06
    On the other hand, 1:08
    if you want to process data once per day, that's called batch processing. 1:09
    Now for instance, maybe you want to process 1:13
    usage data from a whole suite of mobile applications at the end of each day. 1:16
    You won't really be bothered by the latency of getting a response back. 1:20
    And you can prepare the data to be ready when you need it. 1:24
    Data can be very diverse, often containing both structured and unstructured sources. 1:27
    Often, it's made up of many different types. 1:32
    Some of it is dense or sparse and sometimes it's dependent on time and 1:35
    sometimes it's not. 1:38
    There are many other defining characteristics. 1:40
    All of these different properties of data means that processing it 1:42
    can be significantly harder due to the amount of work ahead of time that 1:46
    has to be done to get it into the correct format. 1:49
    The trustworthiness and validity of captured data is not always immediately 1:52
    clear and therefore, can vary greatly, affecting accurate analysis. 1:57
    This could lead to longer pre-processing times and 2:01
    more specific requirements on how much data is necessary to make 2:04
    effective decisions with the tools available. 2:08
    So once again, those four V's are volume, the scale of data, velocity, the speed 2:11
    of data, veracity, the certainty of data and variety, the diversity of data. 2:16
    With these four characteristics in mind, let's explore why big data is so 2:22
    important and why you should be aware of it, right after this quick break. 2:26

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