Spark.rstudio.com is a subdomain of rstudio.com, which was created on 1998-05-15,making it 26 years ago. It has several subdomains, such as docs.rstudio.com cran.rstudio.com , among others.
Description:"Interact with Spark using familiar R interfaces such as dplyr, broom, and DBI. Access Spark's distributed Machine Learning libraries and extend your toolbox with XGBoost, MLeap, H2O, and Graphframes."...
Keywords:sparklyr, R interface, Apache Spark, Machine Learning, XGBoost, MLeap, H2O, Graphframes...
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sparklyr - Spark Machine Learning Library (MLlib) - RStudio https://spark.rstudio.com/guides/mlib |
sparklyr - Intro to Model Tuning - RStudio https://spark.rstudio.com/guides/model_tuning |
sparklyr - tidymodels and Spark - RStudio https://spark.rstudio.com/guides/tidymodels |
sparklyr - Text modeling - RStudio https://spark.rstudio.com/guides/textmodeling |
sparklyr - Grid Search Tuning - RStudio https://spark.rstudio.com/guides/model_tuning_text |
sparklyr - Sort a Spark DataFrame - RStudio https://spark.rstudio.com/packages/sparklyr/latest/reference/sdf_sort.html |
sparklyr - Spark ML - Tuning - RStudio https://spark.rstudio.com/packages/sparklyr/latest/reference/ml-tuning |
sparklyr - Writes a Spark DataFrame into a Spark table https://spark.rstudio.com/packages/sparklyr/latest/reference/spark_write_table |
sparklyr - Generate Test Stream - RStudio https://spark.rstudio.com/packages/sparklyr/latest/reference/stream_generate_test.html |
sparklyr https://spark.rstudio.com/ |
Spark Machine Learning Library (MLlib) https://spark.rstudio.com/mlib/ |
Manipulating Data with dplyr https://spark.rstudio.com/dplyr |
Troubleshooting https://spark.rstudio.com/troubleshooting |
Guides https://spark.rstudio.com/guides/ |
Deployment - sparklyr - Posit https://spark.rstudio.com/deployment/ |
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Get Started Guides Deployment News Reference Packages Learn more R interface to Apache Spark ™ Interact with Spark using familiar R interfaces, such as dplyr , broom , and DBI . Gain access to Spark’s distributed Machine Learning libraries, Structure Streaming ,and ML Pipelines from R. Extend your toolbox by adding XGBoost , MLeap , H2O and Graphframes to your Spark plus R analysis. Connect R wherever Spark runs: Hadoop, Mesos, Kubernetes, Stand Alone, and Livy . Run distributed R code inside Spark Get Started Welcome new users! Start here to learn how to install and use . Guides How-to” articles to help you learn how to do things such as: connect AWS S3 buckets, handling Streaming Data, create ML Pipelines and others. Deployment Articles on Spark environments. Including AWS EMR, Databricks and...
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