Big Data Analytics

Getting started

Data is everywhere and is generated every time an action is performed by anyone, be it a customer or an employer! Big data refers to large amounts of data and datasets collected from multiple sources. These large volumes of data can be stored and put to use efficiently. These data, if used correctly, can be highly beneficial for the development of the said industry.

What is big data analytics?

Big Data Analysis includes the use of data that vary extensively in size and type. The advancements in software and hardware made it possible to organize large amounts of unstructured data, making big data extremely popular in the early 2000s. In addition to this, the introduction of several other technologies like Hadoop, Spark, and NoSQL databases boosted this process! The field of big data helps in integrating information produced by sensors, networks, and smart devices with emerging technologies like machine learning.

How big data analytics works?

Big data analytics involves a blend of collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their data. Let us look into each of these steps:

1. Data collection

This step refers to the gathering of structured, unstructured, and semi-structured data from a variety of sources including, but not limited to, cloud storage, mobile applications, and in-store IoT sensors. All data except structured data and semi-structured is stored in a data warehouse for easy access. On the other hand, unstructured data is assigned metadata and stored in a data lake.

2. Data processing

The data collected must be organized in order to get desirable results. The exponential growth of data makes data processing a challenging task. Some of the commonly used data processing techniques are batch processing and stream processing. Stream processing is a more expensive and complex technique.

3. Clean data

To improve the data quality the processed data should be formatted correctly and should be checked for redundant and irrelevant data. Improper data can obscure the process and lead to flawed results.

4. Analyse data

Converting big data into usable form is a time-consuming task. During this step, big data is converted into big insight. Several big data analysis methods are used for this, some of which are:

· Data mining: It helps to identify patterns and relationships while sorting through large datasets. This is done by identifying anomalies.

· Predictive analysis: This is the process of using past data to predict the future. It helps identify incoming risks and opportunities.

· Deep learning: It integrates artificial intelligence and machine learning to help find patterns in even the most complex of situations.

Current Trends in Big Data Analytics

Data is all around us, generated knowingly or unknowingly, as we move through each facet of our life. Today, we create more data daily than was possible over decades in the past. To the layman, however, all this may seem irrelevant, inconsequential even.

Although they often go unnoticed, the wondrous applications of big data analytics occur throughout our daily life. With the stories it can tell, the patterns it can reveal, and the glimpses of the future it can provide, big data analytics has proven to be a veritable gold mine in the right hands.

With the digital transition and advancement in almost every modern industry, big data analytics has become a tool usable by all. In industries ranging from Finance and Insurance to Healthcare and Education, and for everything from monitoring financial markets for fraudulent activities to predicting staffing and inventory management in consumer trade, big data analytics plays a crucial role today.

As mentioned previously, one of the many prominent use cases of big data analytics today is predictive analytics. In this field, statistical techniques such as data mining, machine learning, and such are used to analyze large quantities of data to identify the likelihood of future events, recognize possible hazards based on historical data, and so much more.

Thus, it should come as no surprise that during the onset of the coronavirus pandemic, when public health efforts depended heavily on predicting how diseases such as those caused by Covid-19 spread across the globe, big data analytics rose to prominence.

Big data analytics aided in the pandemic efforts by fast-tracking the development of new medicines, tracking and monitoring patterns in virus spread, identifying people at risk of infection, etc.


As you have seen, we have several use cases of big data analytics today and yet, there is so much more to explore in this field. This is what makes it the right time for you to step in and begin your exploration of the fascinating field of big data analytics.

To aid you in your efforts, we have curated some material that will help you get started. A few interesting courses that you could take are as follows:

Big Data Analytics — Coursera

Big Data Analytics — Edx

Big Data Analytics — Udemy

For the avid readers and book lovers among you, some interesting reads on the topic of big data analytics that we would suggest are:

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking


Data Science and Big Data Analytics

Closing Note

We hope that this guide has shed some light on the field of big data analytics and perhaps even sparked your interest in it. We hope that it will serve as a guiding light as you embark on your adventure into this field. As always, we wish you the very best and can’t wait to witness the wonders you will accomplish.



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