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13 Sept 2022

Difference between Data Warehousing and Data Mining

Most of the organizations or businesses consider Data Mining and Data Warehouse as the best option for data analysis and they use this to make way for future growth.

When you’re faced with complex data, there are often two ways to approach it. You can either analyze the data and figure out how it can be used to make better decisions, or you can use data mining and data warehouse techniques to help organize and analyze the data.

Data Warehousing

A data warehouse is a computer system used to store and process data. It can be used to store data in various formats, including text, video, images, or tables. The data warehouse stores all the relevant business information of your company. For example, customer names and addresses, product information about each order they placed, or sales data by month over time.

 The benefits of using a data warehouse include the ability to process your data more quickly and efficiently. It can also help you analyze your data for insights and understanding. Additionally, a data warehouse can help you save on costs by consolidating multiple data stores into one place.

You can use a Data Warehouse to analyze your data using different tools. A good way to analyze your data is by using computer software programs like Microsoft Excel or Google Sheets. These programs allow you to graphically display and analyze your data in ways that are easy to understand and use. Additionally, these programs allow you access to many different fields of information that can help you explore and understand your data more accurately.

You can use the Data Warehouse to process your data in different ways. A data warehouse can help you process your data more quickly and efficiently. It can also help you analyze your data for insights and understanding. In order to use a data warehouse effectively, you need to be familiar with the different software programs that are available to manage the database and store your data.

There are a variety of data warehouses available, each with its own benefits and drawbacks. Here is a brief synopsis of the different types of data warehouses:

  • OLAP Data Warehouse - A data warehouse that specializes in providing OLTP (OLTP-based) solutions, which include analysis and reporting of complex data sets. This type of warehouse is often used by business intelligence (BI) and big data applications.
  • RDBMS Data Warehouse - A relational database management system (RDBMS) data warehouse, which provides an easy way to store and analyze large amounts of information. The advantage to using an RDBMS data warehouse is that it can be used for both fast and deep queries.
  • Hadoop Data Warehouse - A large Hadoop cluster can be used to store and analyze massive datasets, making it perfect for businesses looking to large-scale analytics or machine learning tasks.

Data Mining

Data mining is a type of process in which we extract productive information by using different types of data sets. It is a subset of data science used in various fields including marketing, finance, engineering, and science.

It can be used to find patterns or contradictions in data, which can lead to insights that help you achieve your business goals. By understanding the relationships between different data objects, you can make better and faster decisions. Additionally, by using data mining to improve your teamwork and bottom line, you can achieve great results.

You can use data mining in any way, either you do it manually or automatically, it depends on your choice. Hadoop is a popular open-source software framework using which you can store, access, and manage data. It is the most widely used software in the world to operate large datasets such as gigabytes to petabytes of data.

When data mining has to filter huge amounts of data, it uses artificial intelligence software. Using machine learning (which refers to the process of training an artificial intelligence ) algorithms, it analyzes the data and time and finds the patterns available in the data, on the basis of which the future events are predicted accurately.

More and more industries are looking for innovative ways to use data mining facilities. There are generally three steps in data mining - data collection, model building, and verification and deployment. We can start and use the data mining using the following steps -

  • To use data mining, you need to first get the data you need. This can be done by searching through public or private sources of information, or by simply gathering data yourself.
  • Once you have the data you need, it’s time to look for patterns and trends in the data so that you can understand how it affects different areas of your business or marketing campaign.
  • Prepare plots (graphs) of the data so that you can better see how everything is related and understand where changes might be needed in order to reach your goals.
  • Use machine learning algorithms to help identify patterns and relationships in the data so that you can make more informed decisions faster and with less effort.

Data mining is a powerful tool that can be used to get data that will help you improve your business. By using data mining to get information about what products and services are selling on popular marketplaces, you can make better decisions for your business and company.

Difference between Data Warehousing and Data Mining

Data Mining and Warehousing both work on large datasets and find the patterns available in them and help in doing our business or work in an efficient manner. The role of data mining is to look at the entire dataset while data warehousing is to operate on a subset of that dataset, for example, a client record or a sales report.

There is no doubt that data mining and data warehousing are considered part of data analysis, but the way they work is completely different. The difference between the two can be stated in the following facts -

  • Data Warehousing is known as a database system technique in which data analysis is done whereas data mining is a type of process in which we determine the patterns in the given data
  • Data Warehousing acts as a kind of secure, reliable, scalable, and accessible central data repository whereas data mining gives you the power to make intelligent decisions quickly on those data with pattern findings.
  • In data warehousing, where data is processed to combine forms of data and data as a whole, in data mining, it is the act of extracting important useful sets of data and information.
  • The techniques of data warehousing can only be used by engineers or developers, whereas businessmen can also learn and do the process of data mining with the help of engineers.
  • If you want to use advanced data analytics in a data warehouse then it is not possible because the original state information of the data is not available, on the contrary, advanced data analysis in data mining can be done by using various visualization tools and Python libraries.
  • In data warehousing, the data that is received from time to time works for the collection, whereas in contrast to this, the data available in data mining is repeatedly validated, analyzed, and stored.
  • In data warehousing, the data is extracted and stored in a systematic manner for easy and fast reporting. Various techniques are used in data mining to identify the patterns available by the data processor.
  • In data warehousing, instead of complete data, only summary tables are available to you, so you cannot do accurate data analysis, whereas in data mining you can do all types of accurate data analysis from the entire dataset.
  • In Data Warehouse you can store and manage the data as per your business need and cannot be used for any predictive analysis and forecasting, whereas in Data Mining you can do predictive analysis and forecasting through data analysis.

Best Data Warehouse and Data Mining books


Data Mining and Data Warehousing: Principles and Practical Techniques

Data Mining and Data Warehousing: Principles and Practical Techniques

Book Description

This textbook is written in clear language and it covers the fundamental concepts of data mining and data warehousing in a single section.

Important topics including Information Theory, Decision Trees, Naive Bayes Classifiers, Distance Metrics, Partitioned Clustering, Associate Mining, Data Marts and Operational Data Stores are discussed extensively in this textbook.

The textbook is written to meet the needs of graduate students of Computer Science, Engineering and Information Technology for courses on Data Mining and Data Warehousing.

The understanding of concepts is simplified through lesson exercises and practical examples in this textbook.

Chapters like Classification, Collaborative Mining and Cluster Analysis are discussed in detail along with their practical implementation using Weka and R language data mining tools.

Advanced topics including Big Data Analytics, Relational Data Models and NoSQL are discussed in detail in this textbook.

Book details

Format: Kindle Edition, Paperback
Rating: 4.7 out of 5
Author: Parteek Bhatia
Print Length: 506 pages
Publication Date: 27 June 2019
Publisher: Cambridge University Press
Kindle Price: Rs. 970.20*
Paperback Price: Rs. 565.00*
*Price and stock are correct and available at the time of article publication.

Get it here from Amazon

FAQ - Frequently Asked Question

What is difference between Data Warehouse and Data Mining?

Data Warehouse deals with storing and managing all the data in a database in an organized manner whereas data mining works to extract important and useful data sets and patterns based on it from that database.

What is data warehouse example?

Data warehousing involves keeping data and information acquired from multiple sources in a managed database. e.g. mailing list of a business, customer information list through point of sale system, etc.

What is data mining tools?

Data mining techniques are used to build and test data set based data models. The software used to create and execute this data mining technique is called data mining tool for example - R Studio (framework), Tableau (pattern generator tool), etc.

Is SQL a data warehouse?

Yes, It is a data warehouse which is based on cloud in Enterprise Data Warehouse (EDW) mode. It gives ability to quickly run complex queries across petabytes of data.

What is OLAP used for?

OLAP stands for online analytical processing. It is a multidimensional and super-fast online analytical processing software. It is used for speedy data analysis on large volume of data from a data warehouse.

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Aashutosh Kumar Yadav

By Aashutosh Kumar Yadav

He is a PHP-based UI/Web designer and developer by profession and very interested in technical writing and blogging. He has been writing technical content for about 10 years and has proficient in practical knowledge and technical writing.


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