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MapR Technologies, a provider of big data solutions based on Apache Hadoop, has announced the availability of the MapR Distribution product in Amazon Web Services (AWS) Marketplace.
MapR on AWS provides enterprises with another quick and easy way to execute big data projects in the AWS Cloud. The MapR solution in AWS Marketplace provides a path to a cloud platform for continuous, real-time operational Hadoop with fast launch and integrated billing from AWS, enabling customers to pay for only what they use with the scalability and reliability of AWS.
“Deploying MapR from AWS Marketplace provides customers an easy on-ramp to high performance, enterprise-grade Hadoop in the AWS Cloud,” said Steve Wooledge, vice president of product marketing at MapR, in a statement. “We’ve had an established relationship with AWS through the availability of MapR on EMR and now the addition of our products for purchase on AWS Marketplace expands options for customers who want to run real-time Hadoop applications in the AWS Cloud and impact their business as it happens.”
With this announcement, all three editions of the MapR Distribution — Community Edition, Enterprise Edition and Enterprise Database Edition — are immediately available in AWS Marketplace, including support of Apache Spark for rapid development of batch, interactive and streaming applications. MapR uses AWS CloudFormation templates to create reliable Hadoop clusters on AWS.
“We are pleased customers now have access to purchase and deploy the MapR Distribution product from AWS Marketplace,” said Dave McCann, vice president of AWS Marketplace at Amazon Web Services, in a statement. “The rapidly expanding population of customers buying and deploying third-party software from AWS Marketplace can now benefit from more big data deployment options, while taking advantage of AWS’ global scale, reliability and rapid pace of innovation.”
MapR features like mirroring and MapR-DB table replication enable companies to build sophisticated cloud and on-premises hybrid deployment models, including the ability to “burst” analytic or operational applications into the cloud. Furthermore, native Network File System (NFS) capabilities in MapR make it simple for users to move data in the cloud and integrate wide-ranging data sets and applications.
Meanwhile, in other big data news, WANdisco last week shipped WANdisco Fusion 2.6. WANdisco Fusion is a deployment tool for Hadoop, which enables users to sync Hadoop clusters. Version 2.6 brings new network traffic shaping capabilities, giving administrators greater control over the amount of bandwidth used for replication by each data center. It also helps to prioritize network traffic on the basis of source and target data centers. And it enables users to easily assign higher priority to specific files and directories during replication between data centers.
These new wide-area network (WAN) management capabilities combine with WANdisco Fusion’s patented active-active replication technology, which delivers always-on availability across Hadoop clusters any distance apart. This enables companies to meet stringent service-level agreements.
With WANdisco Fusion’s active-active architecture, all servers and clusters are readable and writeable, always in sync and recover automatically from each other after planned or unplanned downtime. There are no passive read-only backup servers and clusters that are only utilized when the primary active cluster goes offline.
“WANdisco Fusion’s features are critical for enterprises looking to scale-up large Hadoop deployments,” said David Richards, WANdisco CEO and co-founder, in a statement. “Customers tell us they are seeing significant ROI, with savings in hardware costs alone on the order of 50 percent. And while ease-of-use generally isn’t the first thing that comes to mind when one thinks about big data, WANdisco Fusion simplifies the Hadoop experience in a way that is truly unique in this space.”
WANdisco Fusion can be implemented across mixed storage environments, including Oracle BDA, Cloudera, Hortonworks, EMC and MapR.