It has also partnered with SQLstream and has licensed SQLstream Blaze, its streaming analytics platform. The deal between both will possibly aid Amazon Kinesis Analytics developers to get actionable insights from streaming data in real time, using standard SQL.
How does it work?
Customers can get started with Amazon Kinesis Analytics by going to the AWS Management Console and selecting a Kinesis Streams or Kinesis Firehose data stream (both are used to ingest data into Amazon’s cloud). Kinesis Analytics then ingests the data, automatically recognizes standard data formats, and suggests a schema that can be refined using the interactive schema editor. Next, customers use the Kinesis Analytics SQL editor and built-in templates to write SQL queries, and point to where they want Kinesis Analytics to load the processed results.
Reportedly, users can configure Kinesis Analytics to route the results of the query to up to four destinations including Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, or an Amazon Kinesis Stream. It also features anomaly detection and top-K analysis for developers to easily perform advanced analytics. This right here, could lessen a lot of hassles for first time users with standard SQL support which doesn’t require to learn complex processing frameworks and programming languages.
Roger Barga, General Manager, Amazon Kinesis, said, “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.”
“With the addition of Amazon Kinesis Analytics, we’ve expanded what’s already the broadest portfolio of analytics services available and 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 added.
Kinesis Analytics supports three different types of windows, claims the company. Namely, Tumbling which are used for producing periodic reports, Sliding, which are used for monitoring and other types of trend detection and Custom windows that are used when the appropriate grouping is not strictly based on time (while processing clickstream data or server logs).
In a blog post, Jeff Barr, AWS Evangelist also commented:
“When I think of running a series of SQL queries against a database table, I generally think of the data as staying more or less static while the queries come and go pretty quickly. Rows are added, changed, and deleted all the time, but this does not generally matter when considering a single query that runs at a particular point in time. Running a Kinesis Analytics query against streaming data turns this model sideways. The queries are long-running and the data changes many times per second as new records, observations, or log entries arrive. Once you wrap your head around this, you will see that the query processing model is very easy to understand: You build persistent queries that process records as they arrive.”
Presently, the new service is offered in the European, Eastern America, and Western America regions and is priced on the basis or number of processing units required.
