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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 executed code may be tightly integrated into the transformation tool, or it may require separate steps by the developer to manually execute the generated code. Data review is the final step in the process, which focuses on ensuring the output data meets the transformation requirements. It is typically the business user or final end-user of ...
Physical design is the phase where the database is designed. It involves the database environment as well as security. Extract, transform, load (ETL) design and development is the design of some of the heavy procedures in the data warehouse and business intelligence system.
Design of CRFs needs to take into account the information required to be collected by the clinical trial protocol and intended to be included in statistical analysis. Where available, standard CRF pages may be re-used for collection of data which is common across most clinical trials e.g. subject demographics.
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail ...
In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed (the "deltas") so that action can be taken using the changed data. The result is a delta-driven dataset .
The engineering design process, also known as the engineering method, is a common series of steps that engineers use in creating functional products and processes. The process is highly iterative – parts of the process often need to be repeated many times before another can be entered – though the part(s) that get iterated and the number of such cycles in any given project may vary.
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).