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The additional complexity cost of pipelining may be considerable if there are dependencies between the processing of different items, especially if a guess-and-backtrack strategy is used to handle them. Indeed, the cost of implementing that strategy for complex instruction sets has motivated some radical proposals to simplify [[computer architecture]], such as [[RISC]] and [[VLIW]]. [[Compiler]]s also have been burdened with the task of rearranging the machine instructions so as to improve the performance of instruction pipelines.
== New
It's true that in recent years the demands on applications and their underlying hardware have been significant. For example, building pipelines with single node applications that trawl through the data row by row is no longer feasible with the volume and variety of [[big data]]. However, with the advent of data analytics engines such as [[Hadoop]], or more recently [[Apache Spark]], it's been possible to distribute large datasets across multiple processing nodes, allowing applications to reach heights of efficiency several hundred times greater than was thought possible before. The effect of this today is that even a mid-level PC using distributed processing in this fashion can handle the building and running of big data pipelines.<ref>[https://www.datapipelines.com/blog/what-is-a-data-pipeline/ What is a Data Pipeline?] Published by Data Pipelines, retrieved 11 March 2021</ref>
== See also ==
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