Ads
related to: etl requirements and steps in testing and training
Search results
Results From The WOW.Com Content Network
A properly designed ETL system extracts data from source systems and enforces data type and data validity standards and ensures it conforms structurally to the requirements of the output. Some ETL systems can also deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions. [1]
Its purpose is to establish a foundation for all the following activities in the lifecycle. Kimball et al. makes it clear that it is important for the project team to talk with the business users, and team members should be prepared to focus on listening and to document the user interviews. An output of this step is the enterprise bus matrix.
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 ...
The extract, transform, load (ETL) step in populating data warehouses is inherently a batch process in most implementations. Performing bulk operations on digital images such as resizing, conversion, watermarking, or otherwise editing a group of image files. Converting computer files from one format to another.
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format.
A staging area, or landing zone, is an intermediate storage area used for data processing during the extract, transform and load (ETL) process. The data staging area sits between the data source(s) and the data target(s), which are often data warehouses, data marts, or other data repositories.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Development, testing, acceptance and production (DTAP) [1] [2] is a phased approach to software testing and deployment. The four letters in DTAP denote the following common steps: Development: The program or component is developed on a development system. This development environment might have no testing capabilities.