Big Data

  Value of data We watched a fascinating video on YouTube which explained the 5 Vs of Big Data. The 5 V's of Big Data The 5 V's of big data allow data scientists to obtain more value from their data. They include: - Velocity - Volume - Value - Variety - Veracity Velocity covers the speed at which the data is created and how fast it moves. Companies will have large continuous flows of data that must be analysed. For example, many medical devices today are designed to monitor patients and collect data. Whether it's hospital medical equipment or smart devices, data needs to be analysed quickly to offer a solution, especially when a person's health is at risk. Volume is the amount of data that exists. This includes the initial size of the data and the amount of data that is collected. It may often be measured in gigabytes, terabytes, petabytes, exabytes, etc. For example, a business that operates across the Unites States of America generates data for millions of transaction...

 The Reason for the Growth of Big Data In previous posts, I discussed the exponential growth in Big Data collection and analysis. Now I am looking at why it has grown at the speed that it has. In 2024, the amount of data in the world is estimated to be 149 zettabytes. For reference, that is equivalent to 149 billion terabytes. The path to get to this point has been surprisingly short, and the rate of the expansion of Big Data is still accelerating. Due to the accessibility of smartphones increasing, users are more frequently accessing the internet. According to Codelantic, I read that one of the main causes for the growth of Big Data is the Internet of Things (Iot). This is a system of devices or objects that are connected to the internet. This means that the user can interact with a vast number of devices around them. In a chart I read, it showed that by 2025, it was predicted that almost 80 billion IoT devices would be connected to the internet. These would all be transferring, r...

 Growth of Big Data We covered the growth of data throughout the modern era. It is fascinating how much the growth of data is accelerating and the comparison of the technology used to store or analyse that data only 5 or 10 years earlier. In 2011, the entire world created around 1.8 zettabytes. This is equivalent to 1.8 billion terabytes. By 2020 it was estimated to be 30 times that amount.   Today, Facebook has 2.2 billion monthly active users. This is because of the accessibility of mobile smart devices to the average person. In 2005, a Facebook user would likely have to use their home computer to use the site, however, now it can be accessed essentially anywhere at any time from a mobile phone.

   Historical Development of Big Data- After the 2000s We then looked into more present technology and the data sets that are collected and analysed today. We watched an interesting video which explained how Big Data is utilised and collected in the modern world.  History Of Big Data | Evolution Of Big Data | Big Data For Beginners | Big Data | Simplilearn By the early 2000s, the World Wide Web had been developed and offered new methods for data collection and analysis. For example, Amazon and eBay began to record data from their customers, such as IP location, click rates, and search logs. This began the newer practicalities of data collection, which now include using Big Data to predict what a customer may be interested in buying next. Companies such as Amazon, Meta, McDonald's, Netflix, Starbucks, Walmart, eBay, and Nike utilise Big Data for profitability. Other uses may involve NASA using Big Data to study the universe, cybersecurity firms using it to fight cybercrime...

 Historical Development of Big Data- Before the 2000s 1800s We explored the development of Big Data, focusing on its exponential growth and the technology required to keep up with its demands. I read that in 1880, the technology that was accessible at the time meant that census data took seven years to compile, and thirteen years was predicted for the 1890 census, however in 1888, Herman Hollerith built an early technology that was able to analyse the data from the 1890 census much faster, resulting in the 1890 census only taking six weeks to compile all the data. This trend of improving technology, resulting in faster and more effective data analysis, has followed humanity for the past 150 years. 1900s We watched an interesting video from SIEMENS detailing the history of Big Data Analytics in the 1900s. The History of Big Data Analytics Part 1 – Time is Money In the 20th century, we learnt that there were also false predictions of how larger data sets would be managed. The video ...

 Definition of Big Data Today, we introduced the concept of Big Data and its place in today's world. Big Data can be defined as 'data so large and complex that no traditional data management tools can process or store it effectively.'  It comes in three types: Structured- Large amounts of data organised in a structured format, such as tables with clear rows and columns. Semi-structured- A Vast amount of data that is not stored in traditional, organised ways such as tables but has some organisation such as tags or metadata, making it easier to analyse than without structure.  Unstructured- This is a large amount of data that doesn't have any specific format or structure and is, therefore, difficult to analyse effectively. An example of Big Data could be the data collected from the New York Stock Exchange, which generates about one terabyte of data daily, or data collected by Facebook, which stores over 500 terabytes of data every day. 1. https://www.geeksforgeeks.org/typ...