EMC Corporation today enhanced the industry's first appliance-based unified Big Data analytics offering, the EMC Greenplum Data Computing Appliance (DCA), with a redesign of its analytics-optimized, scalable systems that are used for statistical analysis, predictive modeling and machine learning. Exploding data volumes, new data types and ever-growing competitive challenges have led to radical changes in analytical technologies and a new approach to exploiting data. Decades-old legacy architectures for data management and analytics are inherently unfit for scaling today's Big Data volumes. The combination of burgeoning amounts of data, broad diversity in type and structure, and the need for complex mathematics to unlock value from data have overwhelmed traditional architectures and led to emergence of a new class of analytical platforms.
To address these priorities, the new EMC Greenplum Data Computing Appliance (DCA) Unified Analytics Platform (UAP) Edition analytics appliance enables analysis of both structured and unstructured data together within a single integrated appliance. The new DCA integrates Greenplum Databases for analytics-optimized SQL, Greenplum HD for Hadoop-based processing and Greenplum partner business intelligence, ETL, and analtyics applications within a single appliance. The integrated solution greatly expands the system's analtyics capabilities and solution flexibility at a fraction of the total cost of ownership of competitive "product portfolio" strategies from Oracle, IBM or Teradata.
The new DCA offers the power of a massively parallel processing (MPP) architecture, while delivering the fastest data-loading rate and the best price/performance ratio in the industrywithout the complexity and constraints of proprietary hardware. It delivers 70+ percent performance gains over the prior generation for data loading and scanning, and 100 percent performance increases for concurrent query workloads, maintaining Greenplum's standing as the industry's leading analytics performance for large, mixed workloads. Enterprises can grow their DCAs as their demand for processing capacity grows or as their analytics requirement evolves.