Amazon Web Services Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), today announced High Storage instances, a new Amazon Elastic Compute Cloud (Amazon EC2) instance family optimized for applications requiring fast access to large amounts of data. These new instances provide customers with 35 EC2 Compute Units (ECUs) of compute capacity, 117 GiB of RAM, 48 TB of storage across 24 hard disk drives, and are capable of delivering more than 2.4 GB per second of sequential I/O performance. With large amounts of direct attached storage per instance, these High Storage instances are ideal for data-intensive applications including Hadoop workloads, log processing and data warehousing, and parallel file systems to process and analyze large data sets in the AWS Cloud. To get started with Amazon EC2 and High Storage instances, visit http://aws.amazon.com/ec2.
“As customers move every imaginable workload to AWS, we continue to provide them with additional instance families to meet the requirements of their applications,” said Peter De Santis, Vice President of Amazon EC2. “High Storage instances are the 9th Amazon EC2 instance family and join Cluster Compute instances and High I/O instances as instance families designed to enhance the performance and efficiency of customers' most demanding applications. These new instances also power Amazon Redshift, a new petabyte scale data warehousing service, and will be very important for customers using Amazon Elastic MapReduce to process large quantities of data.”
“We’re very excited about the introduction of High Storage instances. These instances significantly lower the cost of processing large data sets with Elastic MapReduce," said John Schroeder, CEO of MapR Technologies. "MapR’s M3 and M5 Hadoop distributions offer enterprise-grade Hadoop features such as high availability, data snapshotting, mirroring across availability zones, and NFS mounts. High Storage instances are ideal for these distributions, available through Amazon Elastic MapReduce, as they provide customers a low-cost way to quickly and easily process their large data sets.”