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Author: Admin | 2025-04-28
Big Data vs Data Mining is a fascinating topic that delves into the heart of modern Data Analysis. Big Data denotes the enormous volumes of data generated every second, from social media posts to transaction records. But how do we make sense of this overwhelming information? That’s where Data Mining comes in, the process of extracting valuable patterns and insights from these massive datasets. In this blog, we’ll explore the dynamic relationship between Big Data vs Data Mining. Additionally, with Statista predicting a rise in IoT-connected devices by 2025, understanding how they transform raw data into actionable intelligence is crucial. So, let’s dive in and uncover their secrets.Table of Contents 1) What is Big Data? 2) What is Data Mining? 3) Differences Between Big Data and Data Mining 4) Does Big Data Collect or Choose Data?5) Which is Better, Big Data or Data Mining?6) Conclusion What is Big Data? Before you learn the differences between Big Data vs Data Mining, let us first know what Big Data is. As there have been many technological advancements, the amount of data that is generated has increased considerably. Big Data is related to vast datasets, which can be categorised into structured, semi-structured and unstructured data. These large and complex datasets cannot be stored, analysed or processed by regular data processing tools. Big Data is categorised into 5Vs. These are as follows: a) Volume: It refers to the enormous amount of data that is collected from various technological tools and devices. b) Variety: It refers to the types of data that are collected. c) Velocity: The speed at which data is increasing every day is referred to as the velocity in Big Data. To analyse this data swiftly, Big Data technologies are used. d) Veracity: It refers to the data that is accurate and reliable. Big Data is often used to make strategic planning and decision-making based on the accuracy of the data collected and analysed. e) Value: Value in Big Data is defined as the way data is collected, stored, processed and analysed. What is Data Mining? Now, let's understand what Data Mining is in detail. Data Mining is defined as the process of extracting information from a large amount of data and analysing it. It also helps in establishing certain patterns within the data. It is also known as Knowledge Discovery in Databases (KDD). Data Mining uses advanced techniques to find useful information in these datasets. Data Scientists mainly use Data Mining to analyse business operations in an organisation. This process of extracting data involves four stages: a) Gathering Data: Before extracting data, all the relevant data is identified and collected. The types of data that are generated from various sources are assembled in a data lake or data warehouse. From this warehouse, the following steps are carried out. b) Preparing the Data: Unfiltered and raw data are seldom analysed by Data Scientists. To begin the process of Mining, the data collected are filtered – then they are explored, pre-processed, categorised, and
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