Enterprise SQL Server Added to Amazon Cloud
Amazon Web Services Inc. (AWS) unveiled a new Microsoft SQL Server Enterprise Edition offering for the Amazon Elastic Compute Cloud (EC2.)
A blog post authored by exec Jeff Barr yesterday said the new, pre-configured Amazon Machine Image (AMI) improves upon the Standard Edition by adding more computing power and memory. Standard allows for using up to 16 cores and 128 GiB of memory, while Enterprise can go up to 32 cores and 244 GiB of memory available in an extra-large instance.
The Enterprise Edition comes with SQL Server Enterprise Edition 2012 and SQL Server Enterprise Edition 2014, available in several regions, as explained in the AWS Marketplace.
Barr highlighted the following new and unique features of the offering:
- High availability lets users configure a primary database and up to four active, readable secondary databases into an Always-On availability group.
- Self-service business intelligence through Power View, used to interactively explore and visualize data.
- Data quality services let organizational and third-party reference data be used to profile, cleanse and match your own data.
- Online change functionality lets users restore files and file groups, alter schemas and make indexing changes while a database remains online.
"You can run the AMI on-demand or you can purchase an EC2 Reserved Instance with a one- or three-year term," Barr said.
In another data-related move, AWS on the same day announced it had added support for the enormously popular Apache Spark project to its Amazon Elastic MapReduce (Amazon EMR) service. "Amazon EMR is a Web service that makes it easy for you to process and analyze vast amounts of data using applications in the Hadoop ecosystem, including Hive, Pig, HBase, Presto, Impala and others," the company said. "We're delighted to officially add Spark to this list. Although many customers have previously been installing Spark using custom scripts, you can now launch an Amazon EMR cluster with Spark directly from the Amazon EMR Console, CLI or API."
Spark is an open-source, distributed processing framework often used for Big Data workloads. It leverages in-memory caching and optimized execution to boost performance over older Hadoop ecosystem components such as MapReduce, supporting general batch processing, streaming Big Data analytics, machine learning, graph databases, and interactive, ad hoc queries, according to the AWS Spark page.
Among Spark's many components is Spark SQL for low-latency, interactive SQL queries.
Posted by David Ramel on 06/17/2015