A data warehouse is a central repository of information that can be analyzed to make more informed decisions. It is a system used for reporting and data analysis and is considered a core component of business intelligence. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications.
Large amounts of data from multiple sources are centralized and consolidated into a data warehouse. Its analytical capabilities allow organizations to derive valuable business insights from their data to improve decision-making. Over time, it builds a historical record that can be invaluable to data scientists and business analysts. Because of these capabilities, a data warehouse can be considered an organization’s “single source of truth.”
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Data warehouses offer the overarching and unique benefit of allowing organizations to analyze large amounts of variant data and extract significant value from it, as well as to keep a historical record.
They can analyze data about a particular subject or functional area (such as sales).
Data warehouses create consistency among different data types from disparate sources.
Once data is in a data warehouse, it’s stable and doesn’t change.
Data warehouse analysis looks at change over time.
The architecture of a data warehouse is determined by the organization’s specific needs. They can be achieved in a variety of tiers.
Single-tier architecture is hardly used in the creation of data warehouses for real-time systems. They are often used for batch and real-time processing to process operational data. A single-tier design is composed of a single layer of hardware with the goal of keeping data space at a minimum.
In a two-tier architecture design, the analytical process is separated from the business process. The point of this is to increase levels of control and efficiency.
A three-tier architecture design has a top, middle, and bottom tier; these are known as the source layer, the reconciled layer, and the data warehouse layer. This design is suited for systems with long life cycles. When changes are made in the data, an extra layer of review and analysis of the data is completed to ensure there have been no errors.
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