Analyze data in Azure Data Lake Store using familiar-and-powerful Excel 2016

We are excited to announce that as part of the June 2017 updates of Excel 2016, Azure Data Lake Store is now supported as a source of data. Sophisticated and powerful tools like Excel and Power BI are preferred by many Enterprise data analysts to access and analyze data. As enterprises are building cloud-based data…

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Solving the problem of "Problem with the SSL CA cert (path? access rights?)" for R server on HDInsight

R Server on HDInsight is an ideal platform for performing big data analysis using R interface. If you install packages just from the CRAN R repository, you probably won’t meet the problem mentioned in the title. However, if you want to install some package that is still under development, or if you want to use…


Webinar with Talena: Migrate open source big data applications to HDInsight, add backup & restore to your existing apps running on Apache Hadoop & Spark

“Are you building Big data applications using open source platforms such as Apache Hadoop or Spark, but unsure of how to add cloud to your architecture and manage your data assets better?” Join me for a webinar on June 29th, 2017 at 10am PST with Hari (CTO of Talena) as we explore how Talena can help customers migrate their big…

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Run H2O.ai in R on Azure HDInsight

In our previous blog, we introduced H2O.ai on Azure HDInsight. Currently, H2O can run on Azure HDInsight in Python or Scala APIs. However, R doesn’t come out-of-box. R has been popular in data scientist communities and support of R in H2O.ai on Azure HDInsight has been sought after by many of our customers. Today, we…


Microsoft R Server 9.1 on HDInsight is available!

Today, we are excited to announce that Microsoft R Server 9.1 on Azure HDInsight is generally available. With this, we bring the power and innovation of our latest 9.1 release to the cloud on Spark 2.1 on HDInsight 3.6. This release of R Server on HDInsight includes the following features: State of the art new parallel machine…


Managing Your Azure Data Lake Analytics Compute Resources (Job-level Policy)

In Managing Your Azure Data Lake Analytics Compute Resources (Overview) and Account Level Policy, we gave an overview of many ways by which ADLA helps you manage your compute capacity. We also went in to the details of account level policies. In this blog, let’s dig deeper in to job-level policies. Job-level Policies With job-level…


Managing Your Azure Data Lake Analytics Compute Resources (Account-level Policy)

In Managing Your Azure Data Lake Analytics Compute Resources (Overview), we introduced why customers ask for ways to manage their ADLA compute capacity and what capabilities we have provided to help them achieve their goals. In this blog, let’s go in to more details about account-level policies. ADLA supports three types of account-level policies: Maximum…


Managing Your Azure Data Lake Analytics Compute Resources (Overview)

Azure Data Lake Analytics (ADLA) is a powerful job service that allows organizations to run small or large U-SQL analytics jobs, on demand. You only pay for the compute capacity that you request for those jobs. Because the capacity is automatically scaled to fulfil the job requirements, there is no need to provision capacity for…


Announcing Microsoft Machine Learning Library for Apache Spark

This post is authored by Roope Astala, Senior Program Manager, and Sudarshan Raghunathan, Principal Software Engineering Manager, at Microsoft. This is a cross post and its original post is in Cortana Intelligence and Machine Learning Blog. We’re excited to announce the Microsoft Machine Learning library for Apache Spark – a library designed to make data scientists…