AWS’s New Launch, Kinesis Analytics, to Simplify Real-Time Data Analysis

Amazon’s AWS platform recently launched a new service called Kinesis Analytics to simplify analysis of real-time streaming data by leveraging standard SQL queries. This service is an outcome of building further on AWS’s Kinesis real-time streaming data platform, using which, developers can assimilate streaming data in their applications.

The problem with regular database query is that it views data that is more or less static. Kinesis Analytics can change this by running a Kinesis Analytics query against data that is streaming. By running continued SQL queries to filter and manipulate incoming data as soon as it arrives, Kinesis Analytics will empower developers to add greater value to this data.

The chief focus of Kinesis Analytics is to work with real-time streaming data. However, for those who want to have some delay and scrutinize data batches as and when they arrive, aggregated data is better as it makes it easier to spot trends. For user cases like these, Kinesis Analytics allows the setting of three kinds of windows namely tumbling windows, sliding windows, and for those for whom these two windows do not work, there is the third option — custom windows.

Kinesis Analytics adds yet another feather to AWS’s cap in terms of projects that offer server-less processing. Typical uses for Kinesis Analytics include real-time log analytics, IoT applications, audience tracking systems, and ad exchanges. In addition, since everything is done in SQL, the need for learning a new language or installation of another SDK to use it are eliminated.

The regions where Kinesis Analytics is available now are North Virginia, Oregon, and Ireland. Depending on the number of processing units needed, the pricing of the service is fixed accordingly. Currently, each unit is priced at $0.12 per hour in Amazon’s Irish data center, and $0.11 per hour in the regions of the US. These prices are however subject to change, depending on varying requirements.

This article was originally published on MarTech Advisor