Are you a person who has a Facebook/Twitter account? Can you quantify the amount of data created every day through these different platforms?
Today, 45 zettabytes of data generated every day would soon pile up to 175 zettabytes of data by 2025 as per the new reports published by IDC and sponsored by Seagate.
Now first, let us understand what is this Big Data before we proceed ahead?
Understanding what Big Data is?
When it comes to the definition of Biga Data, in simple words, it is a group of larger volumes of data that is collected from different sources for analysis. Also, this data continues to grow exponentially with each day. This data, however, cannot be stored & managed by our traditional tools.
Let us understand Big Data through simple examples.
Consider the data that generates from the Google search engine. You will be shocked to know around 42,000 searches are made every second. That means while you have finished reading this line, more than 42,000 searches completed.
Even while reading this paragraph, videos of almost 300 minutes have already uploaded on YouTube.
Every day Terabytes of stock data are created at National and New York Stock Exchanges.
All these forms of data cannot be lost. We must analyze this data for predictions of customer demands and creating a competitive product satisfying this demand.
Different classification of Big Data
1. Structured Data
Structured data has a fixed format. The database is used to save this data. Also, it is easier to process such type of data.
A simple example of structured data is storing students’ details in table school on the Oracle database.
2. Unstructured Data
Unstructured Data does not have a format or pattern they typically follow. It isn’t easy to process such data and retrieve meaningful information that can help in predictive analysis.
A typical example of this type of data is the search results obtained while surfing on Bing. It is a combination of text files, images, and videos.
3. Semistructured Data
Semistructured Data does not follow the tabular format associated with database tables but has markups/hashtags as separators. It is simpler to interpret this type of data.
A perfect example of this type is the content written in an HTML file.
The 5V’s of Big Data
When we discuss Big Data, the first thing that comes to mind is the size/volume of data. The volume of Big Data is enormous and needs effective management.
It is the first factor considered to decide whether a given set of data is Big Data or not.
Then comes the velocity, the rate at which data generates every second. Currently, Big Data is increasing exponentially every minute. So this data needs to be processed and analyzed at lightning speed too.
We mustn’t miss the information as the flow of this data is unstoppable.
As of today, we have millions of sources of data generation. We now have structured data like employee table that can easily be stored in the database. Simultaneously, we also have unstructured data like images, videos, emails, and so on that do not follow any particular format. It is not easy to get useful information from unstructured data.
But then we need to ensure that we use the right analytics tool to retrieve information for our business from all varieties of data.
Tons of valuable data is lost as it cannot be adequately processed. So the Big Data that is created from multiple sources needs to be processed accurately to derive this data’s real value. It can be of immense use in scientific or medical research, businesses, and so on.
There is a large amount of data that is generated through WhatsApp messages or on social media. Many times they don’t have an authentic source of information.
So it becomes essential that we authenticate these data sources and extract the most reliable and valuable information from them.
Leading Five Big Data technologies for future
1. Artificial Intelligence
This branch of computer science working tirelessly to develop machines that can behave like humans based on the large data sets fed to their systems for improved learning.
For example, self-driven cars, Alexa, and so on.
2. R Programming
It is a free tool that is available to Data Miners and Data Analysts. It is the most simple language that is available to manage and store Big Data. It also provides a facility to represent the information graphically on computers or paper.
It is the first choice among Data Analyst and is used in critical business scenarios.
3. Data Lakes
The Data lake is an available storehouse where all format, structured or unstructured data is stored in its rawest form. This data can be transformed using the dashboard for analytical, reporting, and visualization purposes.
For example, there is a private Data Lake created at a University in Cardiff, where it collects individual users’ data and organizes them at a single point.
This type of database can handle data that cannot be directly stored in Relation Database Management Systems. It can store and retrieve unstructured data and give better performance when dealing with Big Data. This type of database can support many languages that are similar to SQL and not the only SQL.
It is primarily used to handle dynamic web applications and Big Data.
5. Predictive Analysis
The essential use of Big Data is predicting future market trends. It is achieved using Machine Learning, Data Mining, Mathematical modelling, and so on.
The data pattern study will help us to identify the risk in the future. As a result, we will be able to devise mechanisms through which these risks are minimized.
The purpose of this whole blog was to make you familiarize yourself with the concept of Big Data; the vast data created every day is used to benefit your business/ research.
We also learned about Structured, Unstructured, and Semi-structured types of Big Data.
Most importantly, we understood Big Data’s characteristics, i.e. Volume, Velocity, Value, Variety, and Veracity.
Also, now we know that if we want to become a Data Analyst, we need to learn technologies like R Programming, NoSQL, etc.
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