What is the Difference Between Data Mining and Data Warehouse?

Data mining and data warehousing

Data mining and data warehousing are two frequently used terms in the data analytics field. Both of these technologies play a significant role in the analysis of large amounts of data. As more organizations are now adopting the concept of data-driven decision-making, data analysis has become even more important for businesses. With this, the demand for data analytics professionals is also rising in almost every organization and industry, making it a lucrative career option to consider.

Data analysts are not just in high demand but are also highly paid and valued at their organizations. However, it is essential to have a conceptual clarity of the field by enrolling in a data analytics training program. To make a career in data analytics, you must understand the terms data mining and data warehousing. Moreover, it is also important to have a clear understanding of how the two are different from each other.

What is Data Mining?

Data mining is an important part of data analytics that allows you to find hidden patterns and correlations between different sets of data. It is a computer-supported process that helps in analyzing massive datasets. In this process, computer systems work on the data to extract meaningful information from it. By looking for patterns and trends in data, it tries to predict future behavior. Data mining is mostly useful in corporate analysis, market management & analysis, fraud detection, and risk management.

Businesses use data mining tools to identify patterns in organizational data, view business behaviors, and make important business decisions. These tools utilize statistical techniques, Artificial Intelligence, Machine Learning systems, and databases to discover relationships between data sets. This information is further used to identify business opportunities as well as risks in order to make better data-driven strategies.

Features of Data Mining

Some most important features of data mining are listed below:

  • It deals with large amounts of data & databases and works effectively on them.
  • It automates the prediction of patterns based on the analysis of past trends and behaviors.
  • It makes expected predictions that help businesses in getting answers to questions that are traditionally time-consuming.
  • Data mining creates actionable information.

Advantages of Data Mining

Data mining has a lot of advantages in the data analytics field and some most significant ones are as follows:

  • Data mining techniques are more cost effective and efficient compared to other statistical methods and data analysis techniques.
  • Data mining helps in various sorting and analysis processes. One of its best implementations is the identification of undesired faults in the system.
  • Data mining can help businesses in making market predictions. For example, data mining tools can be used to predict which customers are most likely to buy a certain type of product.
  • With data mining techniques, you can identify fraudulent calls, emails, and transactions.

What is Data Warehousing?

Data warehousing is a technology that is used to combine structured data from one or multiple sources so that it becomes easier to compare and analyze this data. In simple words, a data warehouse can be defined as a computer system having a large data storage capacity. The data stored in warehouses can be used for data mining and hence, in making important business decisions.

Data Warehouses consolidate different types of data while ensuring that the data is consistent and accurate. Warehousing separates analytical processes from transactional databases in order to improve the performance of the system. Data flows from various databases into a warehouse and then the warehouse organizes data into a schema. This schema describes the type and layout of data.

Features of Data Warehouses

Some most important features of data warehouses are listed below:

  • Different types of data present in data warehouses provide information about a specific period.
  • Data warehouses are built using data generated from multiple heterogeneous sources.
  • Data warehouses are non-volatile, i.e. the data can not be changed once it is entered.
  • Data warehouses are subject-oriented, i.e. they provide information about a specific subject.

Advantages of Data Warehousing

Data warehousing plays an important role in the data analytics process and has a lot of advantages. Some of its significant advantages are as follows:

  • Data Warehouses allow you to improve the performance and productivity of data analysis.
  • Data warehousing is cost-efficient and provides you with more accurate data access.
  • Data warehouses provide consistent and quality data and also make any form of organizational data easier to understand.
  • Data warehouses have the capacity to update the data constantly and at frequent intervals.
  • Data warehouses contain massive volumes of historical data that organizations can use to evaluate trends at different periods of time and make predictions for the future.

Data Mining vs Data Warehouse

Data mining and data warehousing are two related but different terms in data analysis. While data warehousing is about storing data from multiple sources, data mining is the process of analyzing large datasets. You can understand the difference between data mining and data warehousing via the following table:

Data Mining Data Warehousing
  • Data mining is the process of analyzing large data sets to identify patterns and understand the relationship between different data sets.
  • Data warehousing is a system to store data from multiple heterogeneous sources.
  • Data analysis takes place at regular intervals.
  • Data storing takes place on a periodical basis.
  • In data mining, the pattern recognition logic is used to identify patterns in data.
  • In data warehousing, data is extracted from multiple sources and stored together to allow easier reporting.
  • An ML model takes the input, breaks it into different parts, and solves each part to produce the final output.
  • Deep learning models just take the input and produce the end result.
  • Data warehousing is entirely carried out by engineers.
  • Data mining is carried out by business leaders/analysts in collaboration with engineers.
  • Data mining focuses on extracting data from large data sets.
  • Data warehousing is the processing of consolidating all the relevant data in one place.

Learn Data Analytics at Edvancer

Edvancer is one of the top online platforms for career-oriented education in India. You can find the following data analytics certification programs on Edvancer and enroll in any one that seems to fulfill your requirements:

With these certifications, you can get comprehensive theoretical as well as practical coverage of the data analytics field. Along with strengthening your concepts via online classes, these courses allow you to develop your practical skills by working on real industry projects. At Edvancer, you also get the option to choose one of the two learning styles (live online classes and self-paced learning) at your convenience.


1. What are the different types of data warehouses and data mining?
Ans. There are three main types of data warehouses, including Operational Data Store (ODS), Data Mart, and Enterprise Data Warehouse (EDW). Data mining can be classified into two basic parts, including Descriptive Data Mining Analysis and Predictive Data Mining Analysis.

2. What are OLAP and OLTP in data mining?
Ans. OLAP (Online Transaction Processing) and OLTP (Online Transaction Processing) are two different types of data processing systems that are designed to serve different purposes. OLTP is useful in real-time update and transaction processing whereas OLAP helps in analyzing complex data sets and reporting.

3. What is an OLTP example?
Ans. Some most common examples of OLTP systems include internet banking applications, ATM machines, online ticket booking and reservation systems, etc.

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