Forrester Research Ranks MapR as Leader in Big Data Hadoop Distributions
SAN JOSE, CA: MapR Technologies, a Big Data solutions provider, announces that Forrester Research has listed MapR as a leader of Hadoop distribution in its report- The Forrester Wave: Big Data Hadoop Distributions, Q1 2016. MapR has achieved the highest score in the current offering architecture criterion.
The report reveals that MapR Technologies delivers extreme performance and reliability at scale through the strategy of engineering a distribution, allowing Hadoop to reach its maximum performance and scale potential with minimum effort.
The report highlights the MapR file system which implements the HDFS API and can store trillions of files (versus the complex configuration for HDFS that requires separated namespaces).
Citing the tremendous efforts of MapR Technologies in the efficient distribution of Hadoop for large-cluster implementations, Forrester reveals that customers are planning large, mission-critical Hadoop clusters and want to use MapR-DB and MapR Streams - which implement the HBase and Kafka APIs, respectively.
“Architectural innovations in the MapR Converged Data Platform enable our customers to power business-critical apps that require immediate analysis of data and 24/7 uptime using Hadoop, Spark, and more. Unlike other Hadoop distributions that run separate clusters for multiple applications, MapR is built to process distributed files, database tables, and event streams in one unified cluster. This advanced architecture uniquely supports real-time operational and analytic apps, significantly reducing costs and complexity as customers scale their big data deployments,” says Jack Norris, Chief Marketing Officer, MapR Technologies.
MapR Technologies provides a unique converged data platform that integrates Hadoop and Spark with global event streaming, real-time database capabilities and enterprise storage. The MapR Platform is a fast, reliable, secure and open data infrastructure that lowers TCO and enables global real-time data applications.