Amazon chooses Cloudera for stream analytics
Amazon has added support for Cloudera’s open source Impala project to its cloud-based Hadoop distro as part of a push to help customers gain faster insight into their growing troves of unstructured information. Amazon Elastic MapReduce (EMR) had previously only worked with Hive, an abstracted warehousing layer that Facebook cooked up back in 2009 to accommodate fast moving social networking data.
Like Hive, Impala uses a dialect of SQL to let users run ad hoc queries against their clusters without having to learn new technologies or get bogged down in the complexity of MapReduce. The software provides significantly higher performance for a number of different workloads, including interactive analytics and data visualization.
“Impala’s performance makes it a great engine for iterative queries and many popular BI tools. With Amazon EMR, you can use Impala as a reliable data warehouse to execute tasks such as data analytics, monitoring, and business intelligence,” an official announcement read.
Amazon’s decision to integrate with Impala is a major win for Cloudera, which faces tough competition in the emerging SQL-on-Hadoop market. The company is going up against Hadapt, which pioneered the technology in 2011, MapR’s Drill and the ambitious Stinger initiative from Hortonworks, to name a few alternatives.
From the AWS standpoint, the addition of structured querying to EMR represents a major milestone towards securing a foothold beyond infrastructure-as-a-service. This latest update comes a few weeks after the cloud titan released Kinesis, a fully managed service for processing streaming data from multiple sources. The offering made its debut at the re:Invent conference alongside RDS for PostreSQL and set of enterprise-oriented features including cross-region snapshotting and network isolation.
The day before, Amazon introduced a virtual desktop service called WorkSpaces along with AppStream, which lets mobile developers offload computationally intensive tasks like rendering to the cloud.
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