MapR Technologies, Inc., provider of the top-ranked distribution for Apache Hadoop, today launched at Hadoop Summit the industry’s first Hadoop application gallery. Launching with solutions from a wide range of Hadoop ecosystem partners, the MapR App Gallery is designed to help customers derive greater business value from big data as they scale-out their enterprise data architectures.
Aimed at end-user developers, administrators, and analysts who want to develop more sophisticated big data capabilities to enhance their business operations and decision-making, the MapR App Gallery offers the benefit of ready-made big data utilities and applications.
“Our customers typically run many different applications on a MapR cluster and the App Gallery makes it much easier and faster to achieve success,” said Jack Norris, chief marketing officer, MapR Technologies. “There is already a broad range of applications available, and we anticipate the number of applications will grow – particularly those leveraging unique capabilities that enable customers to better optimize revenue, control costs and mitigate risk.”
The App Gallery includes administrator-oriented apps for provisioning, management, and security; developer-oriented applications, query engines and frameworks; and analyst-focused applications for business intelligence and machine learning. Partners and solutions represented in the MapR App Gallery today include:
• Database: Hadapt, HP Vertica, Rainstor, and Splice Machine
• Analytics and Data Integration: Alpine Data Labs, Appfluent, Data Tactics, Datameer, DataTorrent, Informatica, Information Builders, Jaspersoft, Pentaho, Platfora, Revelytics, Revolution Analytics, Syncsort, Tableau, and Talend
• Search: Elasticsearch and LucidWorks
• Machine Learning: 0xdata, Skytree, and Zementis
• Management and security: Dataguise, Splunk, StackIQ, and Voltage
The MapR Distribution is the most open distribution for Hadoop with industry-standard APIs to make it plug-and-play without special-purpose connectors or porting. Developers are encouraged to participate by leveraging one or more of the following open interfaces in their applications:
• Storage access via HDFS API and NFS
• Job execution via YARN, Tez, MapReduce, and Spark
• Ecosystem interfaces including HBase, HiveQL, PigLatin
• SQL via Drill, Hive, Stinger/Tez
• Administration interfaces including REST
App Gallery contributors verify that their applications interoperate with the MapR Distribution with documentation and support information provided for each application. Developers can submit new applications directly to the App Gallery at www.mapr.com/appgallery/submit-app.
Supporting Quotes:
“The advanced sensitive data discovery and data-centric protection capabilities inherent in Dataguise are the perfect complement to the multi-tenancy and security of the MapR platform,” said Patty Nghiem, VP of marketing and business development at Dataguise. “We are excited to showcase our integration with the MapR Sandbox for Hadoop in the MapR App Gallery, which will help drive the implementation of secure, compliant production environments based on Hadoop.”
“With our comprehensive platform for big data integration and analytics, we’re proud to be involved in the launch of the new MapR App Gallery,” said Eddie White, EVP of business development at Pentaho. “Companies interested in generating greater business value from their MapR Hadoop investment can turn to this resource for exploring a wide range of best-in-class applications and tools that interoperate with the MapR Distribution.”
“Talend is working closely with MapR to provide best-in-class data integration solutions, natively optimized for enterprise Hadoop environments,” said Francois Chiche, VP global alliances at Talend. “The MapR App Gallery serves as a online destination to help customers understand how a variety of Hadoop technologies can be put into production to optimize big data analysis.”
MapR Technologies and Syncsort partner to optimize enterprise data architecture with Apache Hadoop
MapR and Syncsort, a global leader in Big Data software,_x000D_