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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]
In computer programming, dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. [1]
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 ...
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.
Initially, Google Data Studio and Looker operated as separate products within Google. Google Data Studio's offering was a simple, low-cost, and easy way to connect data sources and create dashboards, [ 11 ] while Looker offered a more enterprise-focused solution with robust support for transformations and permissions.
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Dataflow architecture is a dataflow-based computer architecture that directly contrasts the traditional von Neumann architecture or control flow architecture. Dataflow architectures have no program counter, in concept: the executability and execution of instructions is solely determined based on the availability of input arguments to the instructions, [1] so that the order of instruction ...
In software and systems development, control-flow diagrams can be used in control-flow analysis, data-flow analysis, algorithm analysis, and simulation. Control and data are most applicable for real time and data-driven systems.