Category Archives: Spark

Up and running with Apache Spark on Apache Kudu

Categories: CDH Data Ingestion Data Science General Hadoop How-to Impala Kudu Spark Training Use Case

After the GA of Apache Kudu in Cloudera CDH 5.10, we take a look at the Apache Spark on Kudu integration, share code snippets, and explain how to get up and running quickly, as Kudu is already a first-class citizen in Spark’s ecosystem.

 

As the Apache Kudu development team celebrates the initial 1.0 release launched on September 19, and the most recent 1.2.0 version now GA as part of Cloudera’s CDH 5.10 release,

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How-to: Automate Your sparklyr Environment with Cloudera Director

Categories: Cloudera Manager Data Science Hadoop How-to Ops and DevOps Spark

Since the launch of sparklyr, working with Apache Spark in Apache Hadoop has become much easier for R users. sparklyr contains a dplyr interface into Spark and allows users to leverage crucial machine learning algorithms from Spark MLlib and H2O Sparkling Water. This greatly reduces the barrier of entry for R users in adopting Spark as a tool for big data and should go a long way in enabling R workloads to migrate to Hadoop.

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Achieving a 300% speedup in ETL with Apache Spark

Categories: Data Ingestion General Hadoop HDFS Spark

A common design pattern often emerges when teams begin to stitch together existing systems and an EDH cluster: file dumps, typically in a format like CSV, are regularly uploaded to EDH, where they are then unpacked, transformed into optimal query format, and tucked away in HDFS where various EDH components can use them. When these file dumps are large or happen very often, these simple steps can significantly slow down an ingest pipeline. Part of this delay is inevitable;

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How-to: Do Scalable Graph Analytics with Apache Spark

Categories: Data Science Graph Processing How-to Spark

Get started with scalable graph analysis via simple examples that utilize GraphFrames and Spark SQL on HDFS.

Graphs—also known as “networks”—are ubiquitous across web applications. As a refresher, a graph consists of nodes and edges. A node can be any object, such as a person or an airport, and an edge is a relation between two nodes, such as a friendship or an airline connection between two cities.

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Introducing sparklyr, an R Interface for Apache Spark

Categories: Data Science Guest Spark

Earlier this week, RStudio announced sparklyr, a new package that provides an interface between R and Apache Spark. We republish RStudio’s blog post below (see original) for your convenience.

sparklyr-illustration

Over the past couple of years we’ve heard time and time again that people want a native dplyr interface to Spark, so we built one! sparklyr also provides interfaces to Spark’s distributed machine learning algorithms and much more. 

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