What Are The Features Of Data Warehousing

What Are The Features Of Data Warehousing?

Features Of Data Warehousing
Features Of Data Warehousing

What are the main features of data warehousing? What are the different applications of data warehousing? What are the other advantages and disadvantages of data warehousing? What are the possible applications? What are the possible designs, like the grid, the 3-tier architecture? What are the different design ideas for data warehousing?

Data Warehousing is a term used to refer to the safe, managed storage of data by an organization or other company. The primary purpose of data warehousing would be to build a massive repository of historical data, which is then used to give valuable insight into the organization’s operations. Data warehousing helps improve overall business performance by providing information about customer habits, key performance indicators (KPIs), sales figures, and the like. This data aids managers in formulating better plans for future operations. Some of the major types of data warehousing are:

Oracle Data Warehouse (Dows)

Oracle Dows provides a platform on which various other Oracle data warehouse applications can be developed and supported. Many components are present in an Oracle data warehouse, including data analysis tools, transformation services, and data warehouse management services. An application can have its dedicated server, while some services may be hosted on a web server. A user can choose between an in-house or an external architecture for designing and deploying a data warehouse. The data warehouse usually consists of one or more specialized data processing engines, data analysis tools, query and transformer engines, and one or more business rulesets. Data warehousing can either be stand-alone, or it can have the capability to be integrated with other Oracle database products such as Oracle Enterprise Manager (OBM) and Oracle Enterprise Reporting (ER).

Business Intelligence Data Warehousing: 

Similar to Dows, it collects actionable insights from a wide variety of data sets and processes them into valuable reports. Business intelligence data warehouses can be built to satisfy the requirements of any specific business. The logic usually used in making these data warehouses combines multiple previously built data warehouse specifications. In addition to the components mentioned above, Business Intelligence data warehouses may also contain entity models, financial modules, and userland procedures. Business intelligence data warehouses are an essential ingredient in any business data system and play a crucial role in the quality of business users’ decision-making.

Oracle Data Fusion: 

Similar to Dows, Data Fusion collects various data types from multiple sources and creates a single data warehouse containing all the relevant data. It allows users to access the data from different sources and at the same time. This data warehouse is a highly complex creation that stores all the data and information about one or more businesses or entities. It can also consolidate data obtained from various other data sources and stores them in one place.

Machine Learning: 

Another popular form of data analytics involves training a computer to gather, analyze, and display large data sets automatically. Machine learning uses deep learning, a form of artificial intelligence, in which the laptop enables a human to train it to recognize patterns and make predictions. Companies that need more data but do not have the time to prepare a full-fledged data warehouse can opt for data warehouses that have already been built using machine learning technology.

Modern Data Warehouse Technologies: 

In this modern age of computing, companies do not necessarily need to have a data warehouse to store and analyze the massive amounts of data produced every day. With the help of modern data warehouse technologies, companies can save a lot of time and money by leveraging the power and efficiency of a large, high-performance computing machine. In addition, modern data warehouse technologies make it possible for companies to analyze large amounts of data with minimal effort. To make things even better, machine learning analytics also uses artificial intelligence, significantly improving the accuracy of the resulting predictive models.

Conclusion;

IBM WebSphere Information Processing ( Watson): One of the world’s most famous data warehousing software programs, IBM Watson can help businesses extract the appropriate data from various sources and analyze data to provide quick, accurate, and insightful results. Watson is available in both desktop and web editions. Users can utilize it to perform document and task analysis, speech recognition, image processing, and advanced data mining. This is just but one among the various types of data warehouses available today. The kind of warehouse being used must be determined based on the business requirement and the analytical processing speed of the system.

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