_an-update-on-scaling-data-science-applications-with-sparkr-in-2018heiko-korndorf-wireframe.jpg "An Update on Scaling Data Science Applications with SparkR in 2018Heiko Korndorf (Wireframe) from how to make wireframe Video Screenshot Preview")
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⏲ Duration: 19 min 86 sec
✓ Published: 12-Jun-2018
✓ Published: 12-Jun-2018
Description:
Spark has established itself as the most popular platform for advanced scale-out analytical applications. It is deeply integrated with the Hadoop ecosystem, offers a set of powerful libraries and supports both Python and R. Because of these reasons Data Scientists have started to adopt Spark to train and deploy their models. When Spark 1.4 was released back in 2015, it included the new SparkR library: this API gave R users the exciting new option to run R code on Spark.nnAnd while the initial pr
Spark has established itself as the most popular platform for advanced scale-out analytical applications. It is deeply integrated with the Hadoop ecosystem, offers a set of powerful libraries and supports both Python and R. Because of these reasons Data Scientists have started to adopt Spark to train and deploy their models. When Spark 1.4 was released back in 2015, it included the new SparkR library: this API gave R users the exciting new option to run R code on Spark.nnAnd while the initial pr
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