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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 ...
The architecture for the analytics pipeline shall also consider where to cleanse and enrich data [10] as well as how to conform dimensions. [1] Some of the benefits of an ELT process include speed and the ability to more easily handle both unstructured and structured data.
In software engineering, a pipeline consists of a chain of processing elements (processes, threads, coroutines, functions, etc.), arranged so that the output of each element is the input of the next. The concept is analogous to a physical pipeline .
In computing, multiple instruction, single data (MISD) is a type of parallel computing architecture where many functional units perform different operations on the same data. Pipeline architectures belong to this type, though a purist might say that the data is different after processing by each stage in the pipeline.
A pioneer dataflow language was BLOck DIagram , published in 1961 by John Larry Kelly, Jr., Carol Lochbaum and Victor A. Vyssotsky for specifying sampled data systems. [9] A BLODI specification of functional units (amplifiers, adders, delay lines, etc.) and their interconnections was compiled into a single loop that updated the entire system ...
The Pipeline Open Data Standard (PODS) Pipeline Data Model provides the database architecture pipeline operators use to store critical information and analysis data about their pipeline systems, and to manage this data geospatially in a linear-referenced database which can then be visualized in any GIS platform.
A container represents an application or a data store; Component diagrams (level 3): decompose containers into interrelated components, and relate the components to other containers or other systems; Code diagrams (level 4): provide additional details about the design of the architectural elements that can be mapped to code.
A data architecture aims to set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration , for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems.