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  2. Dataflow programming - Wikipedia

    en.wikipedia.org/wiki/Dataflow_programming

    A traditional program is usually represented as a series of text instructions, which is reasonable for describing a serial system which pipes data between small, single-purpose tools that receive, process, and return. Dataflow programs start with an input, perhaps the command line parameters, and illustrate how that data is used and modified ...

  3. Extract, load, transform - Wikipedia

    en.wikipedia.org/wiki/Extract,_load,_transform

    [1] [2] Since the data is not processed on entry to the data lake, the query and schema do not need to be defined a priori (although often the schema will be available during load since many data sources are extracts from databases or similar structured data systems and hence have an associated schema). ELT is a data pipeline model. [3] [4]

  4. Extract, transform, load - Wikipedia

    en.wikipedia.org/wiki/Extract,_transform,_load

    A properly designed ETL system extracts data from source systems and enforces data type and data validity standards and ensures it conforms structurally to the requirements of the output. Some ETL systems can also deliver data in a presentation-ready format so that application developers can build applications and end users can make decisions.

  5. Stream processing - Wikipedia

    en.wikipedia.org/wiki/Stream_processing

    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 ...

  6. Pipeline (computing) - Wikipedia

    en.wikipedia.org/wiki/Pipeline_(computing)

    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 ...

  7. Amazon Elastic Block Store - Wikipedia

    en.wikipedia.org/wiki/Amazon_Elastic_Block_Store

    The Amazon Data Lifecycle Manager is an automated mechanism that can back up data from EBS volumes, creating and deleting EBS snapshots on a predefined schedule. [8] Elastic Volumes makes it possible to adapt volume size to an application's current needs, using Amazon CloudWatch and AWS Lambda to automate volume changes.

  8. Staging (data) - Wikipedia

    en.wikipedia.org/wiki/Staging_(data)

    The data staging area sits between the data source(s) and the data target(s), which are often data warehouses, data marts, or other data repositories. [ 1 ] Data staging areas are often transient in nature, with their contents being erased prior to running an ETL process or immediately following successful completion of an ETL process.

  9. Amazon Redshift - Wikipedia

    en.wikipedia.org/wiki/Amazon_Redshift

    Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services. [1] It is built on top of technology from the massive parallel processing (MPP) data warehouse company ParAccel (later acquired by Actian), [2] to handle large scale data sets and database migrations. [3]