Data Warehousing and Data Mining – How Do They Differ?

data warehouse interview questionsAn ore mine is excavated and the ore is mined through an elaborate scientific process to extract the useful minerals and metals. A data warehouse is similar to a mine and is the repository and storage space for large amounts of important data. Data warehousing is the process of centralizing, compiling, and organizing large amounts of data collected from multiple sources into one common, central database. It describes the process of designing the storing of the data, such that the reporting and analysis of data becomes easier.

Data mining follows the process of data warehousing. The data compiled in the data warehouse, which are collected as analytics, historical, or customer data are mined to detect meaningful patterns and extract inferences from them. Thus, both data mining and data warehousing are business intelligence tools which play important roles in handling databases and used for turning information or data into actionable knowledge.

Why Data is so Important to Businesses?

Modern day businesses handle and process humongous amounts of data, which can be gathered either in-house or from external sources. With the advent of internet, web, and mobile devices the main challenge of the decade is to manage this huge resource of unstructured or raw data, which is getting generated every moment at a very fast pace. Unstructured data streams rapidly and constantly from different sources and is heterogeneous and variable in format. Unstructured data comprise of all data flowing in from customer interactions on websites, marketing applications running on websites, social networks, e-commerce sites, blogs, and responses from surveys and feedback. This data can be dug into and used to uncover customer consumption patterns, product and brand preferences, and other information which queries and reports can’t effectively reveal. So even with an investment of time and money, it is necessary to store and harness this huge volume of data efficiently. This has given rise to the importance of data warehousing and data mining.

 Online Training Opportunities to Learn About Database

A business’s data is usually stored across a number of databases. Data properly warehoused, is easier to mine. To be able to analyze this broad range of data, each of these databases needs to be connected in some way. If the data warehouse expert designs a data storage system that closely connects the relevant data in different databases, then the data miner can run queries which are faster and more efficient. This means data warehouse experts should know how to connect and relate these data conceptually as well as physically for reporting purposes.

But before embarking on the career as database experts, they should have very good understanding about Database Management System and be able to understand the concepts of databases. Oracle Database Administration for Absolute Beginners  guides how to be a successful Oracle Database Administrator. One keen to know about the basics of MySQL and its architecture can browse through the courses Introduction to MySQL Database and MySQL Database Training for Beginners. It is also important to learn how to properly create and normalize a relational database master database and how to design a database using MySQL. Data storage also requires knowledge on database servers and their maintenance.  SQL Server 2008 R2 Database Maintenance Skills and SQL Server Maintenance Plans guide on how to use SQL Server maintenance plans to perform common database maintenance tasks. It is also important to learn how to protect the data and digital assets of an organization and properly manage and protect a database.

Efficient Data Warehouse Design

Designing a data warehouse is divided into two stages. It involves designing the logical data model and designing the physical data model. Creating the logical data model involves defining various logical entities and the relationship between each entity. The second stage in data warehouse design is creating the physical data model. A good database design should have lesser instances of replicated and inconsistent data. It should also be able to promote data integration and standardization and be able to speed up performance of various database activities.

The database should be scalable and be able to support future expansions of data volumes and types as per the changing business scenario. The data warehouse should be easy to support within moderate costs, and it should be easy to rectify errors and exceptions. The data model should also support fast data recovery.

More Facts on Data Mining

Data mining is the computer-assisted process of using appropriate tools and procedures to analyze the massive data sets and extract meaning and patterns from them. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that are otherwise time-consuming and they scour databases very quickly to find hidden patterns and predictive information that experts may find difficult to find and foresee.

Data mining tells you important things you didn’t know or helps you to visualize the future pattern and trend. The technique that is used for the automated extraction of patterns is called modeling. Modeling is the process of building a model or a set of mathematical relationships and algorithms based on data from situations where the answer is known and then applying the model to other situations where the answers are not known.


Data mining is the process of searching for valuable information in the data warehouse. By using pattern recognition technologies and statistical and mathematical techniques to sift through the warehoused information, data mining helps analysts recognize significant facts, relationships, trends, patterns, exceptions and anomalies that might otherwise go unnoticed. These patterns and relationships discovered in the data help enterprises to make better business decisions, identify sales and consumer trends, design marketing campaigns, predict customer loyalty, and so on. Thus the importance of data warehousing and data mining go hand in hand in present day data centric business scenario.