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The following commit(s) were added to refs/heads/asf-site by this push: new 2e33071 Add Data Mechanics to Powered By 2e33071 is described below commit 2e330710c855f4292cc066c60874a32385a60fb6 Author: Jean-Yves Stephan <jean-y...@datamechanics.co> AuthorDate: Wed Nov 18 12:54:59 2020 -0600 Add Data Mechanics to Powered By Data Mechanics is a managed Spark platform that can be deployed on a Kubernetes cluster inside our customers cloud accounts. We'd love to be on the Powered By Spark page (along other Spark platforms). We contribute to open source projets in the Spark ecosystem (Spark on Kubernetes operator, Data Mechanics Delight). We also use Spark internally for our recommendation engine and logs processing. I tried to be objective / avoid marketing in the description, but I'm open to feedback on changing it. Thanks! Author: Jean-Yves Stephan <jean-y...@datamechanics.co> Closes #299 from jystephan/datamechanics-poweredby. --- powered-by.md | 6 ++++++ site/powered-by.html | 9 +++++++++ 2 files changed, 15 insertions(+) diff --git a/powered-by.md b/powered-by.md index 150d402..d314c88 100644 --- a/powered-by.md +++ b/powered-by.md @@ -88,6 +88,12 @@ and external data sources, driving holistic and actionable insights. - We provided a <a href="https://www.databricks.com/product">cloud-optimized platform</a> to run Spark and ML applications on Amazon Web Services and Azure, as well as a comprehensive <a href="https://databricks.com/training">training program</a>. +- <a href="https://www.datamechanics.co">Data Mechanics</a> + - Data Mechanics is a cloud-native Spark platform that can be deployed on a Kubernetes cluster + inside its customers AWS, GCP, or Azure cloud environments. + - Our focus is to make Spark easy-to-use and cost-effective for data engineering workloads. + We also develop the free, cross-platform, and partially open-source Spark monitoring tool + <a href="https://www.datamechanics.co/delight">Data Mechanics Delight.</a> - <a href="https://datapipelines.com">Data Pipelines</a> - Build and schedule ETL pipelines step-by-step via a simple no-code UI. - <a href="http://dianping.com">Dianping.com</a> diff --git a/site/powered-by.html b/site/powered-by.html index b12cf5f..8b93aaa 100644 --- a/site/powered-by.html +++ b/site/powered-by.html @@ -321,6 +321,15 @@ to run Spark and ML applications on Amazon Web Services and Azure, as well as a <a href="https://databricks.com/training">training program</a>.</li> </ul> </li> + <li><a href="https://www.datamechanics.co">Data Mechanics</a> + <ul> + <li>Data Mechanics is a cloud-native Spark platform that can be deployed on a Kubernetes cluster +inside its customers AWS, GCP, or Azure cloud environments.</li> + <li>Our focus is to make Spark easy-to-use and cost-effective for data engineering workloads. +We also develop the free, cross-platform, and partially open-source Spark monitoring tool +<a href="https://www.datamechanics.co/delight">Data Mechanics Delight.</a></li> + </ul> + </li> <li><a href="https://datapipelines.com">Data Pipelines</a> <ul> <li>Build and schedule ETL pipelines step-by-step via a simple no-code UI.</li> --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org