Tag Archives: Cloudera Data Science Workbench

implyr: R Interface for Apache Impala

Categories: CDH Data Science HBase HDFS Impala Kudu Tools

New R package implyr enables R users to query Impala using dplyr.

Apache Impala (incubating) enables low-latency interactive SQL queries on data stored in HDFS, Amazon S3, Apache Kudu, and Apache HBase. With the availability of the R package implyr on CRAN and GitHub, it’s now possible to query Impala from R using the popular package dplyr.

dplyr provides a grammar of data manipulation,

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Cloudera Enterprise 5.12 is Now Available

Categories: Altus CDH Cloud Cloudera Manager Cloudera Navigator Data Science Hue Impala Kafka Kudu

Cloudera is pleased to announce that Cloudera Enterprise 5.12 is now generally available (GA). The release includes enhancements for running in cloud environments (with broader ADLS support and improved AWS Spot Instance support), usability and productivity improvements for both data science and analytic workloads, as well as performance gains and self-service performance management across a range of workloads.

As usual, there are also a number of quality enhancements, bug fixes, and other improvements across the stack.

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Create conda recipe to use C extended Python library on PySpark cluster with Cloudera Data Science Workbench

Categories: CDH Data Science How-to Spark

Cloudera Data Science Workbench provides data scientists with secure access to enterprise data with Python, R, and Scala. In the previous article, we introduced how to use your favorite Python libraries on an Apache Spark cluster with PySpark. In Python world, data scientists often want to use Python libraries, such as XGBoost, which includes C/C++ extension. This post shows how to solve this problem creating a conda recipe with C extension.

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Getting Started with Cloudera Data Science Workbench

Categories: CDH Data Science

Last week, Cloudera announced the General Availability release of Cloudera Data Science Workbench. In this post, I’ll give a brief overview of its capabilities and architecture, along with a quick-start guide to connecting Cloudera Data Science Workbench to your existing CDH cluster in three simple steps.

At its core, Cloudera Data Science Workbench enables self-service data science for the enterprise. Data scientists can build, scale, and deploy data science and machine learning solutions in a fraction of the time,

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The Benefits of Migrating HPC Workloads To Apache Spark

Categories: CDH Data Science Hadoop Spark

Overview

Recently we worked with a customer that needed to run a very significant amount of models in a given day to satisfy internal and government regulated risk requirements.  Several thousand model executions would need to be supported per hour.  Total execution time was very important to this client.  In the past the customer used thousands of servers to meet the demand.  They need to run many derivations of this model with different economic factors to satisfy their requirements.

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