File Name: difference between data warehouse and data mining .zip
Data Warehousing and Data Mining. Write a program to demonstrate association rule mining using Apriori algorithm Market-basket-analysis.
A data warehouse is a technique for collecting and managing data from varied sources to provide meaningful business insights. It is a blend of technologies and components which allows the strategic use of data. Data Warehouse is electronic storage of a large amount of information by a business which is designed for query and analysis instead of transaction processing. It is a process of transforming data into information and making it available to users for analysis. What Is Data Mining?
Note that this book is meant as a supplement to standard texts about data warehousing. This book focuses on Oracle-specific material and does not reproduce in detail material of a general nature. Two standard texts are:. A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but can include data from other sources. Data warehouses separate analysis workload from transaction workload and enable an organization to consolidate data from several sources.
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java  which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining. This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.
Data warehouse and Data mart are used as a data repository and serve the same purpose. These can be differentiated through the quantity of data or information they stores. The vital difference between a data warehouse and a data mart is that a data warehouse is a database that stores information-oriented to satisfy decision-making requests whereas data mart is complete logical subsets of an entire data warehouse. In simple words, a data mart is a data warehouse limited in scope and whose data can be obtained through summarizing and selecting the data from the data warehouse or with the help of distinct extract, transform and load processes from source data system. Data mart are specific to decision support system application. The data is highly denormalised.
In computing , a data warehouse DW or DWH , also known as an enterprise data warehouse EDW , is a system used for reporting and data analysis , and is considered a core component of business intelligence. They store current and historical data in one single place  that are used for creating analytical reports for workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems such as marketing or sales. The data may pass through an operational data store and may require data cleansing  for additional operations to ensure data quality before it is used in the DW for reporting.
Data warehouse refers to the process of compiling and organizing data into one common database, whereas data mining refers to the process of extracting useful data from the databases. The data mining process depends on the data compiled in the data warehousing phase to recognize meaningful patterns. A data warehousing is created to support management systems.