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KNIME allows users to visually create data flows (or pipelines), selectively execute some or all analysis steps, and later inspect the results, models, using interactive widgets and views. KNIME is written in Java and based on Eclipse. It makes use of an extension mechanism to add plugins providing additional functionality.
Druid is designed to quickly ingest massive quantities of event data, and provide low-latency queries on top of the data. [3] The name Druid comes from the shapeshifting Druid class in many role-playing games , to reflect that the architecture of the system can shift to solve different types of data problems.
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, 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 ...
Dbt enables analytics engineers to transform data in their warehouses by writing select statements, and turns these select statements into tables and views. Dbt does the transformation (T) in extract, load, transform (ELT) processes – it does not extract or load data, but is designed to be performant at transforming data already inside of a ...
There are several main solutions and algorithms used to resolve data hazards: insert a pipeline bubble whenever a read after write (RAW) dependency is encountered, guaranteed to increase latency, or; use out-of-order execution to potentially prevent the need for pipeline bubbles; use operand forwarding to use data from later stages in the pipeline
Systolic arrays (< wavefront processors), first described by H. T. Kung and Charles E. Leiserson are an example of MISD architecture. In a typical systolic array, parallel input data flows through a network of hard-wired processor nodes, resembling the human brain which combine, process, merge or sort the input data into a derived result.
Explicit parallelism is one of the main reasons for the poor performance of Enterprise Java Beans when building data-intensive, non-OLTP applications. [ citation needed ] Where a sequential program can be imagined as a single worker moving between tasks (operations), a dataflow program is more like a series of workers on an assembly line , each ...