Difference Between Big Data vs Data Mining
Big Data refers to a massive volume of data that can be structured, semi-structured, and unstructured. It comprises 5 Vs i.e.
- Volume refers to the amount of data or size of data that can be quintillion when it comes to big data.
- Variety refers to different data types like social media, web server logs, etc.
- Velocity refers to how fast data is growing, exponentially growing, and at a speedy rate.
- Veracity refers to the uncertainty of data like social media and whether the data can be trusted.
- Value refers to what the data we are storing and processing are worth and how we benefit from this vast amount of data.
Big data can be analyzed for insights that lead to better decisions and strategic business moves.
What is Big Data?
Usually, data that is equal to or greater than 1 Tb is known as Big Data. Analysts predict that by 2020, there will be 5,200 Gbs of data on every person worldwide.
Example: On average, people spend about 50 million tweets daily, and Walmart processes 1 million customer transactions per hour.
Why is Big Data Important?
The importance of Big Data does not mean how much data we have but what we would get out of that data. We can analyze data to reduce cost and time, intelligent decision-making, etc.
- Storing such a vast amount of data efficiently.
- How do we process and extract valuable information from this enormous amount of data within a given timeframe?
Solution: Hadoop and Spark framework
What is Data Mining(KDD)?
Data Mining, also known as Knowledge Discovery of Data, refers to extracting knowledge from a large amount of data, i.e., Big Data. It is mainly used in statistics, machine learning, and artificial intelligence. It is the step of the “Knowledge discovery in databases.”
Businesses and government share information they have collected to cross-reference it to find more information about the people tracked in their databases.
The components of data mining mainly consist of 5 levels, those are:
- Extract, transform, and load data into the warehouse
- Store and manage
- Provide data access (Communication)
- Analyze (Process)
- User Interface (Present data to user)
Need for Data Mining::
Analyze relationships and patterns in stored transaction data to get information that will help for better business decisions.
Data mining helps in Credit ratings, targeted marketing, and Fraud detection, like which types of transactions are fraudulent by checking a user’s past transactions and checking customer relationships, like which customers are loyal and which will leave for other companies.
We can do 4 relationships using data mining:
- Classes: It is used to locate the target.
- Clusters: It will group the data items into a logical relation.
- Association: Relationship between data.
- Sequential Pattern: To anticipate behavioral patterns and trends.
Challenges in Data Mining:
- Mining different types of knowledge in databases.
- Handling noise and incomplete data.
- Efficiency and scaling of data mining algorithms.
- Handling relational and complex types of data.
- Protection of data security, integrity, and privacy.
Head-to-Head Comparison Between Big Data vs Data Mining (Infographics)
Below are the Top 8 Comparision between Big Data vs Data Mining
Key Difference Between Big Data vs Data Mining
Below is the difference between Big Data vs Data Mining:
- Big Data and Data Mining are two different concepts; big data is a term that refers to a large amount of data, whereas data mining refers to a deep dive into the data to extract the key knowledge/Patterns/Information from a small or large amount of data.
- The central concept in Data Mining is to dig deep into analyzing the patterns and relationships of data that can be used further in Artificial Intelligence, Predictive Analysis, etc. But the central concept in Big Data is the source, variety, volume of data, and how to store and process this amount of data.
Analyzing big data to give a business solution or define a business is crucial in determining growth.
- We can say that Data Mining need not depend on Big Data as it can be done on small or large amounts of data. Still, big data indeed depends on Data Mining because if we cannot find the value/importance of a large amount of data, then that data is useless.
Big Data vs Data Mining Comparision Table
Given below is the comparison table of Big Data vs Data Mining:
|Feature||Data Mining||Big Data|
|Focus||It mainly focuses on lots of details of data.||It primarily focuses on lots of relationships between data.|
|View||It is a close-up view of the data.||It is the Big Picture of data.|
|Data||It expresses what about the data.||It expresses Why of the data|
|Volume||It can be used for small data or big data.||It refers to a large number of data sets.|
|Definition||It is a technique for analyzing data||It is a concept than a precise term|
|Data Types||Structured data, relational and dimensional database.||Structured, Semi-Structured, and Unstructured data (in NoSQL).|
|Analysis||Mainly Statistical Analysis focuses on the prediction and discovery of business factors on a small scale.||Mainly data analysis focuses on the prediction and discovery of business factors on a large scale.|
|Results||Mainly for strategic decision-making.||Dashboards and predictive measures.|
As we saw, Big data only refers to a large amount of data, and all the big data solutions depend on data availability. It can be considered a combination of Business Intelligence and Data Mining. Data mining uses different tools and software on Big data to return specific results. It is mainly “looking for a needle in a haystack.” In short, big data is the asset, and data mining is the manager used to provide beneficial results.
This has been a guide to Big Data vs Data Mining. Here we have discussed Big Data vs Data Mining head-to-head comparison, key differences, and a comparison table. You may also look at the following articles to learn more –
- Apache hive vs Apache hbase
- Apache Hive vs Apache Spark SQL
- Apache Kafka vs Flume
- Apache Nifi vs Apache Spark