Introduction The {sparklyr} package lets us connect and use Apache Spark for high-performance, highly parallelized, and distributed computations. We can also use Spark’s capabilities to improve and streamline our data processing pipelines, as Spark supports reading and writing from many popular sources such as Parquet, Orc, etc. and most ...

Introduction Apache Spark is an open-source distributed cluster-computing framework implemented in Scala that first came out in 2014 and has since then become popular for many computing applications including machine learning thanks to among other aspects its user-friendly APIs. The popularity also gave rise to many online courses of varied quality. ...

Introduction The National Hockey League (NHL) is considered to be the premier professional ice hockey league in the world, founded 102 years ago in 1917. Like many other sports, the data about teams, players, games, and more are a great resource to dive in and analyze using modern software tools. Thanks to ...

Introduction Functional programming is a programming paradigm where programs are constructed by applying and composing functions and it quite popular in the data science application because of some of its useful properties that can help for example...

Introduction Automating the execution, testing and deployment of R code is a powerful tool to ensure the reproducibility, quality and overall robustness of the code that we are building. A relatively recent feature in GitHub - GitHub actions - allows us to do just that without using additional tools such ...

Introduction Over the past months, we published and refined a series of posts on Using Spark from R for performance with arbitrary code. Since the posts have grown in size and scope the blogposts were no longer the best medium to share the content ...

Introduction It is Christmas time again! And just like last year, what better time than this to write about the great tools that are available to all interested in working with R. This post is meant as a praise to a few selected tools and packages that helped me to ...

Introduction In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs, SQL query generators that can be executed by Spark and how to use the lower-level API to invoke methods on Java object references from R. In this fifth part, ...

Introduction In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs and SQL query generators that can be executed by Spark, how to execute them with DBI and how to achieve lazy SQL statements that only get executed when needed. ...

Introduction In the previous part of this series, we looked at writing R functions that can be executed directly by Spark without serialization overhead with a focus on writing functions as combinations of dplyr verbs and investigated how the SQL is generated and Spark plans created. In this third part, ...

Introduction In the first part of this series, we looked at how the sparklyr interface communicates with the Spark instance and what this means for performance with regards to arbitrarily defined R functions. We also examined how Apache Arrow can increase the performance of data transfers between the R session ...

Introduction Apache Spark is a popular open-source analytics engine for big data processing and thanks to the sparklyr and SparkR packages, the power of Spark is also available to R users. This series of articles will attempt to provide practical insights into using the sparklyr interface to gain the benefits ...

Introduction In the previous post, we focused on setting up declarative Jenkins pipelines with emphasis on parametrizing builds and using environment variables across pipeline stages. In this post, we look at various tips that can be useful when automating R application testing and continuous integration, with regards to orchestrating parallelization, ...

Introduction Jenkins is a popular open-source tool that helps teams with automation and implementation of continuous integration and deployment pipelines, comparable to for example Atlassian’s Bamboo, GitLab CI or to some extent Travis. In this post, we share some practical lessons learned when integrating R applications via Jenkins for ...

Introduction Recently I was involved in a task that included reading and writing quite large amounts of data, totaling more than 1 TB worth of csvs without the standard big data infrastructure. After trying multiple approaches, the one that made this possible was using data.table’s reading and writing facilities ...

Introduction As pointed out by a recent read the R source post on the R hub’s website, reading the actual code, not just the documentation is a great way to learn more about programming and implementation details. But there is one more activity to get even more hands-on experience ...

Introduction As we wrote in Should you start your R blog now?, blogging has probably never been more accessible to the general population, R users included. Usually, the simplest solution is to host your blog via a service that provides it for free, such as Netlify, GitHub or GitLab Pages. ...