Amazon is adding analytics to its Amazon Kinesis Service for real-time data stream capture. This move pairs up live stream data management in the cloud with an ability to perform SQL queries, without the user needing to produce a full analytics application.
In addition to working with Kinesis, Amazon Kinesis Analytics will also work with the Amazon Kinesis Firehose service, which loads a real-time data stream into Amazon S3 storage, the Redshift data warehouse, or Amazon Elasticsearch Service.
Jeff Barr, AWS chief evangelist, wrote in an Aug. 11 blog post: "You can now run continuous SQL queries against your streaming data, filtering, transforming, and summarizing the data as it arrives. You can focus on processing the data and extracting business value from it," instead of setting up virtual servers and application logic to do those things on the data stream.
The analytics service can spot standard data formats within a stream and suggest a schema with which to capture them, and then upload them into S3, Redshift, or Elasticsearch.
Users can go to a Kinesis Analytics console, with which they can select an existing Kinesis data stream or Firehose stream, then write SQL queries to apply to it. Data extracted by the queries can then be forwarded to specific analytics tools or application for further analysis and trigger alerts or reports as designated by the user, according to information on getting started with Kinesis Analytics.
Amazon announced the Kinesis real-time data service at its ReInvent Conference in November 2013, and made it generally available in December 2013. Kinesis Firehose followed as a service in October last year.
[Want to see what early adopters are doing with Kinesis? Read How Amazon Kinesis Adds Speed, Resilience to Analytics.]
Kinesis Analytics makes it conceivable for business analysts or other non-programmers to adapt to AWS callable services, as opposed to waiting for IT staff developers to produce a specific analytics application. Many analysts are capable of composing SQL queries and making use of the results.
Kinesis Analytics is another example of how AWS starts out with a fundamental service in the cloud, which a few hardy pioneers experiment with at first. As it gains traction, AWS adds services that work with it and enhance its value to IT.
"AWS's functionality across big data stores, data warehousing, distributed analytics, real-time streaming, machine learning, and business intelligence allows our customers to readily extract and deploy insights from the significant amount of data they're storing in AWS," Roger Barga, general manager of Amazon Kinesis, wrote in this week's announcement.
"With the addition of Amazon Kinesis Analytics, we've ... made it easy to use SQL to do analytics on real-time streaming data so that customers can deliver actionable insights to their business faster than ever before," Barga wrote.