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A navigational box that can be placed at the bottom of articles. Template parameters [Edit template data] Parameter Description Type Status State state The initial visibility of the navbox Suggested values collapsed expanded autocollapse String suggested Template transclusions Transclusion maintenance Check completeness of transclusions The above documentation is transcluded from Template ...
Data lineage is a track of the life cycle of each piece of data as it is ingested, processed, and output by the analytics system. This provides visibility into the analytics pipeline and simplifies tracing errors back to their sources.
A template processor (also known as a template engine or template parser) is software designed to combine templates with data (defined by a data model) to produce resulting documents or programs. [1] [2] [3] The language that the templates are written in is known as a template language or templating language.
In computing, a pipeline or data pipeline [1] is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines ...
MIL-STD-498 standard describes the development and documentation in terms of 22 Data Item Descriptions (DIDs), which were standardized documents for recording the results of each the development and support processes, for example, the Software Design Description DID was the standard format for the results of the software design process.
As a consequence, data in CMS Pipelines is handled in record mode. For text files, a record holds one line of text. In general, CMS Pipelines does not buffer the data but passes records of data in a lock-step fashion from one program to the next. This ensures a deterministic flow of data through a network of interconnected pipelines.
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The captured lineage is combined and processed to obtain the data flow of the pipeline. The data flow helps the data scientist or a developer to look deeply into the actors and their transformations. This step allows the data scientist to figure out the part of the algorithm that is generating the unexpected output.