In information technology, data warehousing is a collection of procedures and technologies designed to help information processing units (CPUs, memory, hard drives, etc.) store and analyze data. Data warehousing is a generic term that may apply to a broad range of software or hardware processes. In information technology, data warehousing is an essential part of the information management systems (IMS), such as ERPs, Enterprise Resource Planning (ERP), and SQL databases. In computer studies, data warehousing is a methodology for gathering data from a dynamic entity and organizing it for later use by a central data repository. In information technology, data warehousing is used to centralize data from applications that require access to all data storage resources.
In computer studies, data warehousing is usually implemented as a generic approach in large databases. In reality, data warehouses are usually defined as collections of related tables in a server that allows for fast and easy access to data and application logic for frequently accessed data. A data warehouse is usually characterized by rows of interconnected databases wherein data is accessed by a user following a directed path from the root to each piece in the tree. In addition, data warehouses are also classified into two main types: online and offline. The online analytical process extracts data in a centralized database, while the offline analytical process extracts data from multiple portable devices.
In information technology, data warehousing is used to integrate online analytical processing applications with internal, on-demand data warehouses. The concept of data warehousing is not new; however, the advances in information technology have made data warehouses more important for organizations than they have been in the past. Today, companies utilize data warehouses to improve data management, forecast future requirements, generate more accurate forecasts, provide business insight, reduce costs, increase productivity and implement quality improvements. Data warehousing or Data Lake can be defined as the set of tools and methods for managing information that enables users to gain access, query, aggregate, and store data.
As an entity that can be very complex and requires a high degree of specialization, data warehousing has evolved to provide capabilities that were previously available only to information management specialists. Data warehousing provides capabilities like file system integration, relational and object-oriented programming, security, and integration with databases. The typical data warehouse consists of an integrated system database, data repository, collection data, logical schemas, work items, work orders and jobs, linked devices, and operational systems. The system can be very complicated and challenging to understand especially for non-technical users, so most data warehousing is done as part of an operational system rather than as part of an entity that would require extensive documentation.
Data warehousing has many advantages such as providing insights that can help strategic decision-making, increase operational productivity, improve flexibility, and bring about cost savings and increased profitability. Business intelligence tools can also be applied in data warehousing to support strategic business decision-making. Business intelligence tools include process modeling, process re-designing, process optimization, and operational risk assessment among others. These tools are used to support decision-makers in making business decisions by gathering and organizing new data and analyzing past data to identify patterns and relationships. Some of these tools require programming and data visualization expertise but are highly effective when properly implemented.
Today's business environment is highly dependent on information technology and data warehousing is a key component in an organization's information technology infrastructure (ITI). There are two major categories of data warehousing including the traditional data warehouse models based on object-oriented programming (OOP) and the time-variant data warehouses. Time variants include batch, continuous processing, real-time processing, and non-conventional time warehousing.
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