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As such, ETL is a key process to bring all the data together in a standard, homogeneous environment. Design analysis [5] should establish the scalability of an ETL system across the lifetime of its usage – including understanding the volumes of data that must be processed within service level agreements. The time available to extract from ...
The Kimball lifecycle is a methodology for developing data warehouses, and has been developed by Ralph Kimball and a variety of colleagues. The methodology "covers a sequence of high level tasks for the effective design, development and deployment" of a data warehouse or business intelligence system. [1]
Data collection and validation consist of four steps when it involves taking a census and seven steps when it involves sampling. [3] A formal data collection process is necessary, as it ensures that the data gathered are both defined and accurate. This way, subsequent decisions based on arguments embodied in the findings are made using valid ...
Also, most commercial data analysis tools are used by organizations for extracting, transforming and loading ETL for data warehouses in a manner that ensures no element is left out during the process (Turban et al., 2008). Thus the data analysis tools are used for supporting the 3 Vs in Big Data: volume, variety and velocity. Factor velocity ...
Requirements analysis and negotiation – Requirements are identified (including new ones if the development is iterative), and conflicts with stakeholders are solved. Both written and graphical tools (the latter commonly used in the design phase, but some find them helpful at this stage, too) are successfully used as aids.
Business policies that also drive data architecture design include internal organizational policies, rules of regulatory bodies, professional standards, and applicable governmental laws that can vary by applicable agency. These policies and rules describe the manner in which the enterprise wishes to process its data.
Health systems engineering or health engineering (often known as health care systems engineering (HCSE)) is an academic and a pragmatic discipline that approaches the health care industry, and other industries connected with health care delivery, as complex adaptive systems, and identifies and applies engineering design and analysis principles in such areas.
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).