Quick Introduction to Big Data and Analytics

Big Data

Big data is a term that refers to a collection of enormous and complicated data sets and volumes of data. It encompasses massive amounts of data, data management capabilities, social media analytics, and real-time data.

Big data analytics

Big data analytics is the examination of massive volumes of data. There are vast quantities of diverse digital data. The term “big data” refers to the volume of data, and enormous data sets are measured in terabytes or petabytes. This is referred to as Bigdata. Following a thorough examination of Bigdata, the data was launched as Big Data analytics. This article will discuss the five Vs of big data and the techniques and technologies used to manage it.

How data is generated?

On any given day, one million people log onto Facebook. On YouTube, 4.5 million videos are watched each day. It is estimated that 188 million emails are sent each day. That’s a lot of information, so how do you categorise any information as “big data”?

Every month, a single smartphone user generates approximately 40 exabytes of data, which is enormous. If we multiply this number by 5 billion smartphone users, it becomes overwhelming for our brains to comprehend and process. It is difficult for traditional computing systems to cope with this volume of data. This enormous amount of information is referred to as “big data.” Take a look at the amount of data generated on the internet every minute. Snaps are shared at a rate of 2.1 million per second. Google receives 3.8 million search queries every day.

With the help of the 5 Vs concept (volume, velocity, variety, veracity, and value), it is possible to achieve this goal cost-effectively. Allow us to illustrate this with the help of an example from the healthcare industry.


Hospitals and clinics worldwide generate massive amounts of data; each year, 2314 exabytes of data is collected in the form of patient records and test results. All of this data is being generated at a breakneck pace, which contributes to the phenomenon known as big data velocity.


Healthcare data makes up a sizable portion of the data flowing through the world’s wires. That proportion will continue to grow as the Internet of Things, medical devices, genomic testing, machine learning, natural language processing, and other novel data generation and processing techniques evolve.

Certain data, such as patient vital signs in the ICU, must be updated in real-time and shown instantly at the point of care. In these instances, Laney notes, system reaction time is a critical statistic for enterprises and may serve as a differentiation for suppliers building such solutions.


The term “variety” refers to the different types of data available, including structured, semi-structured, and unstructured data. Excel records, log files, and x-ray images are all examples of data storage.


The term “veracity” refers to the accuracy and dependability of the generated data.


The medical industry will benefit from analysing all of this data because it will allow for faster disease detection, better treatment, and lower costs. This is referred to as the value of Big Data; however, how do we store and process all of this data efficiently? We have several frameworks available, including Cassandra, Hadoop, and Spark.

Big Data in Practical Sense

Let’s look at Hadoop and see how it stores and processes data to demonstrate this. Big Data is a term that refers to a large amount of information. To store large amounts of data, Hadoop uses a distributed file system known as Hadoop. If you have a large file, it will be broken down into smaller chunks and stored on several different computers worldwide. Not only that, but when you break a file, you also create copies of it placed in other nodes. This way, you can store your big data in a distributed manner and be confident that your data will be safe even if one machine fails. The MapReduce technique is used to process large amounts of data; this is a time-consuming task broken down into smaller tasks B, C, and D.

Three machines now complete each task in parallel, rather than just one, and then combine the results in an assembly line fashion at the end of the process. Because of this, processing becomes efficient and straightforward. Parallel processing is the term used to describe this.

Now that we have stored and processed our large amounts of data, we can use this information to analyse it for various applications in games such as Halo 3 and Call of Duty. Designers examine user data to determine at what stage most users pause, restart, or quit playing a video game. This information can be used to rework the game’s storyline and improve the user’s experience, which will, in turn, lower the churn rate of the game’s customers.

For example, Big Data was instrumental in disaster management efforts during Hurricane Sandy in 2012. It was used to understand better the storm’s impact on the east coast of the United States, and the necessary measures were put in place. It was able to predict the landfall of Hurricanes five days in advance, which wasn’t possible earlier in the year. These examples demonstrate how valuable big data can be when it is properly processed and analysed. Big data is becoming increasingly valuable. In that case, I have a question for you. When it comes to Hadoop, which of the following statements is incorrect?

A) Hadoop’s storage layer comprises the distributed file system HDFS.
The data is stored in HDFS in a distributed manner, as shown in B).
C) HDFS is capable of performing parallel data processing.
In HDFS, smaller chunks of data are stored on multiple data nodes, which merits consideration.

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