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...These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. For production-grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.
Dataproc templates and pipelines for solving simple in-cloud data task
Dataproc templates are designed to address various in-cloud data tasks, including data import/export/backup/restore and bulk API operations. These templates leverage the power of Google Cloud's Dataproc, supporting both Dataproc Serverless and Dataproc clusters. Google provides this collection of pre-implemented Dataproc templates as a reference and for easy customization.
...Apache Spark integration including GUI configuration, status, progress, interrupt, and tables. One-click publication with interactive plots and tables, and Jupyter Lab. BeakerX is available via conda, pip, and docker. Or try it live online with Binder. All of BeakerX’s JVM languages plus Python and JavaScript have APIs for interactive time-series, scatter plots, histograms, heatmaps, and treemaps.
Spark is a Java library that converts data in Macromedias SWF ("Flash") data format to XML conforming to a specialized DTD and vice versa. The primary goal of Spark is to make it easier to work with SWF in a Java and XML based server environment.
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Python framework for developing distributed web applications.
Composed by a webserver and a number of registered applications, each handling an url family (www.foo.com/*). The applications can run on the same server or on other servers.