| import React, { FC } from 'react'; |
| import PageSection from '../PageSection'; |
| import TwoColumnFeatureSection from '../TwoColumnFeatureSection'; |
| import Subsection from '../Subsection'; |
| |
| const LovedByDevelopers = (props) => ( |
| <> |
| <> |
| <PageSection sectionTitle={"First-class developer support"} sectionSubtitle={"Easy to customize & extend"} |
| backgroundClass={"background-primary-light"}> |
| <p className={"text-center"}>Apache StreamPipes is a great platform for developers: Implement custom adapters, |
| data processors or sinks and install them at runtime.<br/> Use StreamPipes Functions to define processing |
| logic based on real-time IIoT data.<br/> Or use the client libraries, available in Java and Python, to |
| interact with live and historical data in an easy way. |
| </p> |
| <TwoColumnFeatureSection> |
| <Subsection title={"Add your own extensions with the Software Development Kit"}> |
| <p className={"text-left"}>It is easy to extend StreamPipes. Whether you need connectivity to a proprietary |
| data source, want to |
| implement your custom-tailored algorithm as a pipeline element or need a new interface to your third party |
| system: Simply use the SDK to extend the functionality of StreamPipes.</p> |
| |
| <p className={"text-left"}> |
| With its microservice architecture at its core, you can install your extensions at any time without the |
| need to restart the whole system. |
| </p> |
| <div> |
| <a href="/docs/extend-tutorial-data-processors" className="sp-button sp-button-medium sp-button-blue"><i |
| className="fas fa-hand-point-right"></i> Tutorial: SDK</a> |
| </div> |
| </Subsection> |
| <div> |
| <img className="d-block w-100 mt-2 mb-2" src={"/img/screenshots/sdk-data-processor.png"} alt={"Online ML"}/> |
| </div> |
| </TwoColumnFeatureSection> |
| <TwoColumnFeatureSection> |
| <div> |
| <img className="d-block w-100 mt-2 mb-2" src={"/img/screenshots/python-client.png"} alt={"Online ML"}/> |
| </div> |
| <Subsection title={"Interact with StreamPipes through our client libraries"}> |
| <p className={"text-left"}> |
| StreamPipes includes Java and Python libraries which allow you to interact with StreamPipes |
| programmatically. |
| </p> |
| <p className={"text-left"}> |
| You can modify the pipeline lifecycle, receive live data from all connected sources in a unified API, and |
| Data Scientists love the possibility to extract historical data directly into Pandas data frames for |
| in-depth analysis. |
| </p> |
| |
| <p className={"text-left"}>And of course, you can also just use the provided REST interface!</p> |
| |
| <div> |
| <a href="/docs/docs/python" className="sp-button sp-button-medium sp-button-blue"><i |
| className="fas fa-hand-point-right"></i> Python Client</a> |
| </div> |
| </Subsection> |
| </TwoColumnFeatureSection> |
| <TwoColumnFeatureSection> |
| <Subsection title={"Seamlessly integrate AI & Machine Learning"}> |
| <p className={"text-left"}> |
| Our Python client includes an integration with the OnlineML library River, so that you can get started |
| with your custom anomaly detection and other ML features with just a few lines of code. |
| </p> |
| <p className={"text-left"}> |
| But you can also integrate other ML models, and play back the results in form of a new data stream to |
| StreamPipes. |
| </p> |
| <div> |
| <a href="/docs/docs/python/latest/tutorials/4-using-online-machine-learning-on-a-streampipes-data-stream" className="sp-button sp-button-medium sp-button-blue"><i |
| className="fas fa-hand-point-right"></i> Online ML with StreamPipes</a> |
| </div> |
| </Subsection> |
| <div> |
| <img className="d-block w-100 mt-2 mb-2" src={"/img/screenshots/python-onlineml.png"} alt={"Online ML"}/> |
| </div> |
| </TwoColumnFeatureSection> |
| <TwoColumnFeatureSection> |
| <div> |
| <img className="d-block w-100 mt-2 mb-2" src={"/img/screenshots/streampipes-custom-ui.png"} alt={"Online ML"}/> |
| </div> |
| <Subsection title={"Customized User Interfaces"}> |
| <p className={"text-left"}> |
| As a software platform that targets the Industrial IoT, we know that many applications require their own user interface, for instance, to assist maintenance personnel or to visualize machine behaviour. |
| </p> |
| <p className={"text-left"}> |
| The default user interface of StreamPipes can be extended with additional views by an integrated microfrontend framework. |
| </p> |
| <p className={"text-left"}> |
| A Typescript client library and an API to access platform features help you to build your custom IIoT solution with much less programming effort. |
| </p> |
| </Subsection> |
| </TwoColumnFeatureSection> |
| </PageSection> |
| |
| </> |
| |
| </> |
| ) |
| |
| export default LovedByDevelopers; |