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2. Google Cloud Dataflow - Wikipedia

   en.wikipedia.org/wiki/Google_Cloud_Dataflow

   Google Cloud Dataflow was announced in June, 2014 [3] and released to the general public as an open beta in April, 2015. [4] In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. [5]

3. Apache Beam - Wikipedia

   en.wikipedia.org/wiki/Apache_Beam

   Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream (continuous) processing. [2] Beam Pipelines are defined using one of the provided SDKs and executed in one of the Beam’s supported runners (distributed processing back-ends) including Apache Flink, Apache Samza, Apache Spark, and Google Cloud Dataflow.

4. Dataflow - Wikipedia

   en.wikipedia.org/wiki/Dataflow

   Dataflow computing is a software paradigm based on the idea of representing computations as a directed graph, where nodes are computations and data flow along the edges. [1] Dataflow can also be called stream processing or reactive programming. [2] There have been multiple data-flow/stream processing languages of various forms (see Stream ...

5. Google Cloud Platform - Wikipedia

   en.wikipedia.org/wiki/Google_Cloud_Platform

   Google Cloud Platform is a part [8] of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as Google Workspace (G Suite), enterprise versions of Android and ChromeOS, and application programming interfaces (APIs) for machine learning and enterprise mapping services.

6. Cloud analytics - Wikipedia

   en.wikipedia.org/wiki/Cloud_Analytics

   Google Cloud Dataflow unifies programming models and manages services for executing a range of data processing patterns including streaming analytics, ETL, and batch computation. Google Cloud Dataproc manages Spark and Hadoop service, to process big datasets using the open tools in the Apache big data ecosystem.

7. Dataflow programming - Wikipedia

   en.wikipedia.org/wiki/Dataflow_programming

   Data flow has been proposed [by whom?] as an abstraction for specifying the global behavior of distributed system components: in the live distributed objects programming model, distributed data flows are used to store and communicate state, and as such, they play the role analogous to variables, fields, and parameters in Java-like programming ...

8. MapReduce - Wikipedia

   en.wikipedia.org/wiki/MapReduce

   MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...

9. Stream processing - Wikipedia

   en.wikipedia.org/wiki/Stream_processing

   In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation.