Big Data for Beginners: All You Need to Know

  • ·        YouTube views per day: 4,150,000
  • ·        Tweets per day: 456,000
  • ·        Instagram posts per day: 46,740
  • ·        Facebook status updates per day: 293,000

We are surrounded by data. And, unlike a couple of decades ago, this data isn’t just a collection of numbers or stats. Data has evolved a lot, and today, we can see data in the form of videos, pictures, text, URLs and a lot more. On top of this, new data is being generated every second. This makes data handling storage and processing next to impossible using traditional systems.

So, what can we do to ensure that this data is properly handled?

How can you derive insights from data that isn’t numeric?

And, what would be the hierarchy to store this kind of irregular data?

The answer came in the early 2010s in the form of big data. Although the term was in use since the early 1990s, a defined form of big data didn’t see the light of day until two decades since its first appearance in the industry. One of the biggest misconceptions about big data is that it is just the volume of data that categorizes it as big data. This is just partially true as there are a couple of other factors that make big data what it is.

Big data refers to large amounts of data that is pouring from several data sources and doesn’t have a fixed format.

To understand this better, let’s first look at the three wide subdivisions of data formats:

·        Structured: This is the data format which deals with defined data with a fixed schema.

Example: RDBMS

·        Semi-Structured: Organized data that does not have a fixed format is known as semi-structured data.

Example: XML, JSON

·        Unstructured: Data which is unorganized and has an unknown schema is referred to as unstructured data.

Example: Audio, video, images

As inferred earlier, big data deals mostly with semi-structured and unstructured data formats. At its core, big data has the following characteristics:

The above properties of big data are called the five V’s of big data. As with every technology, big data is constantly evolving which means that the V’s also keep evolving.

To help you understand better, here are five more V’s that have been associated with big data:

1.      Validity: Accuracy and correctness of the data

2.      Variability: Non-static or dynamic behavior of the data

3.      Volatility: The data’s tendency to change with respect to time or other factors

4.      Vulnerability: The data’s property of being open to attacks

5.      Visualization: The ability of the data to be represented in terms of charts and graphs

The Hadoop Ecosystem: Big Data Analytics

As discussed earlier, traditional tools and approaches are incapable of handling, analyzing and storing big data. Therefore, a new set of tools was required to work with this new kind of data. There are several different tools out in the market that can be used to work with big data, but the most popular of them all is the Apache Hadoop ecosystem.

The following diagram summarizes what the Hadoop ecosystem looks and functions like:

The big data technology field is one of the most popular and ever-green fields in the IT industry since 2013. Because of the various applications big data has in fields like education, sports, government, automobile, and almost everything else, there is a lot of demand for skilled professionals in the industry.

If you would like to learn more and become a big data expert, why not start with a master’s course online and get into one of the coolest careers in the industry?