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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]
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.
Google Compute Engine Unit (GCEU), which is pronounced as GQ, is an abstraction of computing resources. According to Google, 2.75 GCEUs represent the minimum power of one logical core (a hardware hyper-thread) based on the Sandy Bridge platform. The GCEU was created by Anthony F. Voellm out of a need to compare the performance of virtual ...
Google Workspace (formerly G Suite, formerly Google Apps) is a collection of cloud computing, productivity and collaboration tools, software and products developed and marketed by Google. It consists of Gmail , Contacts , Calendar , Meet and Chat for communication; Drive for storage; and the Google Docs Editors suite for content creation.
Gartner defines a hybrid cloud service as a cloud computing service that is composed of some combination of private, public and community cloud services, from different service providers. [64] A hybrid cloud service crosses isolation and provider boundaries so that it cannot be simply put in one category of private, public, or community cloud ...
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The following is a comparison of cloud-computing software and ... Google Cloud Platform: 2013 Yes ... [31] but they use spam mandrillapp servers. [32] root volume is ...
Modern data centers must support large, heterogenous environments, consisting of large numbers of computers of varying capacities. Cloud computing coordinates the operation of all such systems, with techniques such as data center networking (DCN), the MapReduce framework, which supports data-intensive computing applications in parallel and distributed systems, and virtualization techniques ...