Cloudera Selects Azure as a Preferred Cloud Platform
We are working to make Azure the best cloud platform for big data, including Apache Hadoop. To accomplish this, we deliver a comprehensive set of solutions such as our Hadoop-based solution Azure HDInsight and managed data services from partners, including Hortonworks. Last week Hortonworks announced the most recent milestone in our partnership and yesterday we announced even more data options for our Azure customers through a partnership with Cloudera.
Cloudera is recognized as a leader in the Hadoop community, and that’s why we’re excited Cloudera Enterprise has achieved Azure Certification. As a result of this certification, organizations will be able to launch a Cloudera Enterprise cluster from the Azure Marketplace starting October 28. Initially, this will be an evaluation cluster with access to MapReduce, HDFS and Hive. At the end of this year when Cloudera 5.3 releases, customers will be able to leverage the power of the full Cloudera Enterprise distribution including HBase, Impala, Search, and Spark.
We’re also working with Cloudera to ensure greater integration with Analytics Platform System, SQL Server, Power BI and Azure Machine Learning. This will allow organizations to build big data solutions quickly and easily by using the best of Microsoft and Cloudera, together. For example Arvato Bertelsmann was able to help clients cut fraud losses in half and speed credit calculations by 1,000x.
Our partnership with Cloudera allows customers to use the Hadoop distribution of their choice while getting the cloud benefits of Azure. It is also a sign of our continued commitment to make Hadoop more accessible to customers by supporting the ability to run big data workloads anywhere – on hosted VM’s and managed services in the public cloud, on-premise or in hybrid scenarios.
From Strata in New York to our recent news from San Francisco it’s exciting times ahead for those in the data space. We hope you join us for this ride!
Eron Kelly
General Manager, Data Platform
