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Sparkling Water is out
by Jakub Hava | June 04, 2020 H2O-3 , Sparkling Water

Sparkling Water is about making machine learning simple, speedy, and scalable with Apache Spark. This blog provides an overview of the following new features: No H2O Client on Spark Driver Speedups Automatic String conversion to Categoricals No H2O Client on Spark DriverPreviously, Sparkling Water always started worker nodes eith...

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AI & ML Platforms: My Fresh Look at Technology

2020: A new year, a new decade, and with that, I’m taking a new and deeper look at the technology offers for building AI and machine learning systems. I’ve been interested in since its early days as a company (it was 0xdata back then) in 2014. My involvement had been only peripheral, but now I’ve begun to work with this comp...

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H2O’s AutoML in Spark
by Jakub Hava | July 23, 2018 AutoML , Sparkling Water , Technical , Tutorials

This blog post demonstrates how H2O’s powerful automatic machine learning can be used together with the Spark in Sparkling Water.We show the benefits of Spark & H2O integration, use Spark for data munging tasks and H2O for the modelling phase, where all these steps are wrapped inside a Spark Pipeline. The integration between Spark and...

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Sparkling Water 2.3.0 is now available!
by Team | April 12, 2018 Sparkling Water

Hi Makers! We are happy to announce that Sparkling Water now fully supports Spark 2.3 and is available from our download page . If you are using an older version of Spark, that’s no problem. Even though we suggest upgrading to the latest version possible, we keep the Sparkling Water releases for Spark 2.2 and 2.1 up-to-date with the lates...

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Sparkling Water 2.2.10 is now available!
by Team | March 22, 2018 AutoML , Sparkling Water

Hi Makers! There are several new features in the latest Sparkling Water. The major new addition is that we now publish Sparkling Water documentation as a website which is available here . This link is for Spark 2.2. We have also documented and fixed a few issues with LDAP on Sparkling Water. Exact steps are provided in the documentation...

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New versions of H2O-3 and Sparkling Water available
by Team | December 02, 2017 H2O Release , Sparkling Water

Dear H2O Community, #H2OWorld is on Monday and we can’t wait to see you there! We’ll also be live streaming the event starting at 9:25am PST. Explore the agenda here . Today we’re excited to share that new versions of H2O-3 and Sparkling Water are available. We invite you to download them here: H2O-3.16 – MO...

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Use on Azure HDInsight
by Team | April 18, 2017 Cloud , Sparkling Water , Technical , Tutorials

This is a repost from this article on MSDN. We’re hosting an upcoming webinar to present you how to use H2O on HDInsight and to answer your questions. Sign up for our upcoming webinar on combining H2O and Azure HDInsight. We recently announced that H2O and Microsoft Azure HDInsight have integrated to provide Data Scientists with a Lead...

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Sparkling Water on the Spark-Notebook
by Team | April 10, 2017 Guest Posts , Sparkling Water , Technical

This is a guest post from our friends at Kensu. In the space of Data Science development in enterprises, two outstanding scalable technologies are Spark and H2O. Spark is a generic distributed computing framework and H2O is a very performant scalable platform for AI. Their complementarity is best exploited with the use of Sparkling Wat...

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What is new in Sparkling Water 2.0.3 Release?
by Team | January 05, 2017 Community , H2O Release , Sparkling Water

This release has H2O core – Feature:This architectural change allows to connect to existing h2o cluster from sparkling water. This has a benefit that we are no longer affected by Spark killing it’s executors thus we should have more stable solution in environment with lots of h2o/spark node. We are working on article on ...

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sparklyr: R interface for Apache Spark
by Team | October 07, 2016 Community , R , Sparkling Water

This post is reposted from Rstudio’s announcement on sparklyr – Rstudio’s extension for Spark Connect to Spark from R. The sparklyr package provides a complete dplyr backend. Filter and aggregate Spark datasets then bring them into R for analysis and visualization. Use Spark’s distributed machine learning library from R. Create...

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Spam Detection with Sparkling Water and Spark Machine Learning Pipelines
by Team | June 15, 2016 Sparkling Water , Technical , Tutorials

This short post presents the “ham or spam” demo, which has already been posted earlier by Michal Malohlava , using our new API in latest Sparkling Water for Spark 1.6 and earlier versions, unifying Spark and H2O Machine Learning pipelines. It shows how to create a simple Spark Machine Learning pipeline and a model based on the fitted pipe...

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Databricks and H2O Make it Rain with Sparkling Water
by Team | December 01, 2015 Demos , Sparkling Water

**This blog post was first posted on the Databricks blog hereDatabricks provides a cloud-based integrated workspace on top of Apache Spark for developers and data scientists. has been an early adopter of Apache Spark and has developed Sparkling Water to seamlessly integrate’s machine learning library on top of Spark. In thi...

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