MapR Converged Data Platform delivers performance and reliability at scale
MapR Technologies was placed among the select companies that Forrester Research, Inc. invited to participate in its January 2016 report entitled, The Forrester Wave: Big Data Hadoop Distributions, Q1 2016. In this evaluation, MapR was cited as a Leader and had the highest score in the current offering architecture criterion.
According to the Forrester report, “MapR Technologies innovates to deliver extreme performance and reliability at scale. From day one, MapR Technologies’ strategy has been to engineer a distribution that would allow Hadoop to reach its full performance and scale potential with minimal effort. Enter MapR Technologies’ linchpin – the MapR file system which implements the HDFS API, is fully read/write, and can store trillions of files (versus the complex configuration for HDFS that requires separated namespaces). MapR has also done more than any other distribution vendor under the covers of Hadoop to deliver a reliable and efficient distribution for large-cluster implementations. Its customers typically have or are planning large, mission-critical Hadoop clusters and want to use MapR-DB and MapR Streams (which implement the HBase and Kafka APIs, respectively).”
Only the MapR Converged Data Platform brings together the power of Hadoop and Spark with global event streaming; real-time, top-ranked NoSQL database capabilities; and enterprise storage. The MapR Platform provides the fastest, most reliable, secure and open data infrastructure that dramatically lowers TCO and enables global real-time data applications.
“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,” said Jack Norris, chief marketing officer, MapR Technologies. “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 both real-time operational and analytic apps, significantly reducing costs and complexity as customers scale their big data deployments.”
