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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 ...
In addition to the built-in programs, CMS Pipelines defines a framework to allow user-written REXX programs with input and output streams that can be used in the pipeline. Data on IBM mainframes typically resides in a record-oriented filesystem and connected I/O devices operate in record mode rather than stream mode. As a consequence, data in ...
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.
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.
Other data warehouses (or even other parts of the same data warehouse) may add new data in a historical form at regular intervals – for example, hourly. To understand this, consider a data warehouse that is required to maintain sales records of the last year. This data warehouse overwrites any data older than a year with newer data.
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...
Very long instruction word (VLIW) refers to instruction set architectures that are designed to exploit instruction-level parallelism (ILP). A VLIW processor allows programs to explicitly specify instructions to execute in parallel, whereas conventional central processing units (CPUs) mostly allow programs to specify instructions to execute in sequence only.
DVC pipeline is focused on the experimentation phase of the ML process. Users can run multiple copies of a DVC pipeline by cloning a Git repository with the pipeline or running ML experiments. They can also record the workflow as a pipeline, and reproduce [28] it in the future. Pipelines are represented in code as yaml [29] configuration files ...