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Working with Big Data
Big data
What makes big data different? Most big-data proponents talk about the four
Vs of big data:
1. Volume: The amount of data that we generate and store is growing at an
increasing rate, and predictions of the future generally only suggest further
increases. Today's multi-gigabyte sized hard drives will turn into exabyte
hard drives in a few years, and network throughput traffic will be increasing
as well. The signal to noise ratio can be quite difficult, with important data
being lost in the mountain of non-important data.
2. Velocity: While related to volume, the velocity of data is increasing too.
Modern cars have hundreds of sensors that stream data into their computers,
and the information from these sensors needs to be analyzed at a subsecond
level to operate the car. It isn't just a case of finding answers in the volume of
data; those answers often need to come quickly.
3. Variety: Nice datasets with clearly defined columns are only a small part
of the dataset that we have these days. Consider a social media post, which
may have text, photos, user mentions, likes, comments, videos, geographic
information, and other fields. Simply ignoring parts of this data that
don't fit your model will lead to a loss of information, but integrating that
information itself can be very difficult.
4. Veracity: With the increase in the amount of data, it can be hard to determine
whether the data is being correctly collected—whether it is outdated, noisy,
contains outliers, or generally whether it is useful at all. Being able to trust
the data is hard when a human can't reliably verify the data itself. External
datasets are being increasingly merged into internal ones too, giving rise to
more troubles relating to the veracity of the data.
These main four Vs (others have proposed additional Vs) outline why big data is
different to just lots-of-data. At these scales, the engineering problem of working
with the data is often more difficult—let alone the analysis. While there are lots of
snake oil salesmen that overstate the ability to use big data, it is hard to deny the
engineering challenges and the potential of big-data analytics.
The algorithms we have used are to date load the dataset into memory and then
to work on the in-memory version. This gives a large benefit in terms of speed of
computation, as it is much faster to compute on in-memory data than having to load
a sample before we use it. In addition, in-memory data allows us to iterate over the
data many times, improving our model.
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