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  2. Microservices - Wikipedia

    en.wikipedia.org/wiki/Microservices

    It is common for microservices architectures to be adopted for cloud-native applications, serverless computing, and applications using lightweight container deployment. . According to Fowler, because of the large number (when compared to monolithic application implementations) of services, decentralized continuous delivery and DevOps with holistic service monitoring are necessary to ...

  3. Service-oriented architecture - Wikipedia

    en.wikipedia.org/wiki/Service-oriented_architecture

    Services in a microservice architecture [42] are processes that communicate with each other over the network in order to fulfill a goal. These services use technology agnostic protocols , [ 43 ] which aid in encapsulating choice of language and frameworks, making their choice a concern internal to the service.

  4. Deployment flowchart - Wikipedia

    en.wikipedia.org/wiki/Deployment_flowchart

    While deployment flowcharts can be drawn by hand using pen and paper, various software tools include functionality to construct the flowcharts on computer. These include products such as Microsoft Visio. [5] As with other process mapping techniques, deployment flowcharts require a certain degree of detail (and accuracy) to provide useful benefit.

  5. 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]

  6. RapidMiner - Wikipedia

    en.wikipedia.org/wiki/RapidMiner

    RapidMiner uses a client/server model with the server offered either on-premises or in public or private cloud infrastructures.. RapidMiner provides data mining and machine learning procedures including: data loading and transformation (ETL), data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment.

  7. Cloud computing - Wikipedia

    en.wikipedia.org/wiki/Cloud_computing

    Cloud bursting is an application deployment model in which an application runs in a private cloud or data center and "bursts" to a public cloud when the demand for computing capacity increases. A primary advantage of cloud bursting and a hybrid cloud model is that an organization pays for extra compute resources only when they are needed. [ 68 ]

  8. Cloud mining - Wikipedia

    en.wikipedia.org/wiki/Cloud_mining

    Cloud mining is the process of cryptocurrency mining utilizing a remote data center with shared processing power. [1] Cloud mining has been used by ransomware groups and scammers to launder cryptocurrency. [2] This type of cloud mining enables users to mine bitcoins or alternative cryptocurrencies without managing the hardware.

  9. KNIME - Wikipedia

    en.wikipedia.org/wiki/KNIME

    KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.