Big data analytics is the use of advanced analytic
techniques against very large, diverse big data sets
that include structured, semi-structured and
unstructured data, from different sources, and in
different sizes from terabytes to zettabytes.
What is big data exactly? It can be defined as data
sets whose size or type is beyond the ability of
traditional relational databases to capture, manage
and process the data with low latency. Characteristics
of big data include high volume, high velocity and
high variety. Sources of data are becoming more
complex than those for traditional data because they
are being driven by artificial intelligence (AI),
mobile devices, social media and the Internet of
Things (IoT). For example, the different types of data
originate from sensors, devices, video/audio,
networks, log files, transactional applications, web
and social media — much of it generated in real time
and at a very large scale.
With big data analytics, you can ultimately fuel
better and faster decision-making, modeling and
predicting of future outcomes and enhanced business
intelligence. As you build your big data solution,
consider open source software such as Apache Hadoop,
Apache Spark and the entire Hadoop ecosystem as
cost-effective, flexible data processing and storage
tools designed to handle the volume of data being
generated today.
