What is a Data Warehouse?
In simple terms, a data warehouse can be defined as a relational database that is primarily designed for query and analysis instead of transaction processing. More often than not, a data warehouse contains historical data derived from transaction data, though it can pull data from other sources as well. Thus, the analysis workload is differentiated from the transaction workload, which helps the enterprises in consolidating the data they have compiled from a number of sources.
How it helps in data analysis?
On leveraging a data warehouse, an organization can generate a consolidated view of its enterprise data that has already been optimized for reporting and analysis. In other words, it can be well assumed that a data warehouse is an aggregated and squeezed copy of the entire transaction and nontransaction data that has specifically been structured for dynamic queries and fast, efficient business analytic.
The process and tools
Apart from a relational database, a data warehouse environment must comprise of an online analytical processing (OLAP) engine; an extraction, transportation, transformation, and loading (ETL) solution; client analysis tools, and a number of additional applications that allow the enterprises to effectively gather data and deliver it to their business users.
During its process, the warehouse obtains data and information from heterogeneous production data sources either along with their generation or periodically, which allows the system to run queries over data that originally came from different sources in a smooth and streamlined manner. It converts the data gathered into business ready, meaningful information that is ready to fulfill all enterprise reporting requirements for all levels of users. The data warehouses can further deliver the interactive content to anyone in the extended enterprise, such as customers, partners, employees, managers, and executives.
Characteristics of a Data Warehouse
Core characteristics of a data warehouse pertain to the different approaches adopted by it, and the primary goals it aims to achieve. Usually, a data warehouse must be subject oriented. For instance, in order to learn more about its sales data, a company can build a warehouse that concentrates on sales. Using this, the queries like “Who was the best customer for a certain item last year?” can be easily resolved. This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.
Similarly, it’s imperative that a data warehouse is seamlessly integrated with its subject orientation. Data warehouses must put data from disparate sources into a consistent format. It helps in effective prevention of naming conflicts and inconsistencies among units of measure. A data warehouse must also be nonvolatile, which means that the data must not alter after it is once entered into the warehouse. The core purpose of a data warehouses are to enable an enterprise to analyze what has occurred, hence nonvolatility of the data definitely plays an important role in the process.
Humongous amounts of data are required for analyzing business trends rapidly in the ever expanding world of today. This symbolizes stark difference from the online transaction processing (OLTP) systems, where old data is required to be moved to an archive after the processes are over. Hence, the focus of a data warehouse on change over time effectively proves the fact that time variance is a yet another important and necessary feature that must be exhibited by data warehouses in both policy and practice.
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