There are various tools to facilitate the project are as follows −Data Warehouse Bus Architecture Matrix − The matrix produced by the design team in their internal meetings can be cleaned up and used as a presentation support for meetings with several designers, authority, and end-users. The matrix is very useful as a high-level introduction to the design. It provides each audience a look of what the eventual function of the data warehouse will develop into.Fact Table Diagram − After preparing Bus Architecture the matrix, it can prepare a logical diagram of each completed fact table. The fact table not ... Read More

There are the following methods for designing an Individual Fact Table which is as follows −Choosing the Data Mart − It can be choosing the data mart in the simplest method is the same as choosing the legacy source of information. Typical data marts involve purchase orders, shipments, retail sales, payments, or user connections. These can be an instance of single-source data marts.In some cases, it can define a data mart that should contain multiple-legacy sources. The instance of a multiple-source data mart is user profitability, where legacy sources that define revenue should be combined with legacy sources that represent ... Read More

Dimensional modeling is a logical design method that follows to present the data in a standard structure that is perceptive and enables high-performance access. It is genetically dimensional and observes to a discipline that needs the relational model with several restrictions.Each dimensional model is composed of one table with a multipart key, known as the fact table, and a group of smaller tables known as dimension tables. Each dimension table has an individual element primary key that correlates to one of the elements of the multipart key in the fact table. This distinctive star-like structure is known as star join. ... Read More

There are various approaches of Business Dimensional Lifecycle which are as follows −Project planning − Project planning addresses the description and scoping of the data warehouse project, such as readiness evaluation and business justification. These are the tasks because of the high visibility and costs related to data warehouse projects.Project planning targets resource and skill-level staffing requirements, coupled with project task assignments, continuation, and sequencing. The resulting integrated project plan recognizes all tasks related to the Business Dimensional Lifecycle and the parties included. It can deliver as the foundation for the ongoing administration of the data warehouse project. Project planning ... Read More

Data staging is a major process that includes the following subprocesses which are as follows −Extracting − The extract step is the first phase of getting information into the data warehouse environment. Extracting defines reading and learning the source data, and copying the elements that are required to the data staging area for more work.Transforming − Because the data is extracted into the data staging area, there are several possible transformation processes, are as follows −It can be cleaning the data by correcting misspellings, resolving domain conflicts (including a city name that is inconsistent with a postal code), dealing with ... Read More

There are various elements of a data warehouse system which are as follows −Source System − An operational system of data whose service it is to capture the transactions of the business. A source system is known as a “legacy system” in a mainframe environment.The features of the source system are uptime and availability. Queries opposite to source systems are definite, “account-based” queries that are elements of the normal transaction flow and firmly restricted in their demands on the legacy system.Data Staging Area − A storage area and group of processes that simple, transform, combine, de-duplicate, household, archive, and produce ... Read More

Data mining is the procedure of exploration and analysis of huge quantities of data to find meaningful patterns and rules. On the other hand, web mining defines the process of using data mining techniques to extract useful data patterns and trends from web-based records and services, server logs, and hyperlinks. Read this article to learn more about Data Mining and Web Mining and how they are different from each other. What is Data Mining? Data mining is the process of discovering meaningful new correlations, patterns, and trends by shifting through a large amount of data stored in repositories, using pattern ... Read More

In businesses, in order to predict future issues, it is very important to analyze the past and present data. For this purpose, there are several data analysis techniques available like data mining and statistics.Data mining and statistics are used for making data-driven decisions; these are basically the primary components of data science. Data mining and statistics may seem to be similar, but they are quite different from each other. Read this article to learn more about Data Mining and Statistics and how they are different from each other. What is Data Mining? Data miningis the technique of exploration and analysis ... Read More

Data mining and data science are the two most important concepts in information technology. Data mining is a process of determining useful information, trends, and patterns from large databases, so that these parameters can be used to solve several business problems. On the other hand, data science is the process of obtaining important insights from the unstructured and structured data by using different analysis tools. Basically, data science is one of the modern emerging fields of computer science and information technology for the study of largescale data analysis. Read this article to learn more about Data Mining and Data Science ... Read More

Big Data represents the vast amount of data that can be structured, semi−structured, and unstructured sets of data ranging in terms of terabytes. In contrast, Data Mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Data mining utilizes tools like machine learning, visualization, statistical models, etc. to extract the useful data from the Big Data. Read this article to find out more about Data Mining and Big Data and how they are different from each ... Read More