Oracle Stream Analytics (OSA) is a graphical tool that provides “Business Insight into Fast Data”. In layman terms, that translates into an intuitive web-based interface for exploring, analysing, and manipulating streaming data sources in realtime. These sources can include REST, JMS queues, as well as Kafka. The inclusion of Kafka opens OSA up to integration with many new-build data pipelines that use this as a backbone technology.
Previously known as Oracle Stream Explorer, it is part of the SOA component of Fusion Middleware (just as OBIEE and ODI are part of FMW too). In a recent blog it was positioned as “[…] part of Oracle Data Integration And Governance Platform.”. Its Big Data credentials include support for Kafka as source and target, as well as the option to execute across multiple nodes for scaling performance and capacity using Spark.
I’ve been exploring OSA from the comfort of my own Mac, courtesy of Docker and a Docker image for OSA created by Guido Schmutz. The benefits of Docker are many and covered elsewhere, but what I loved about it in this instance was that I didn’t have to download a VM that was 10s of GB. Nor did I have to spend time learning how to install OSA from scratch, which whilst interesting wasn’t a priority compared to just trying to tool out and seeing what it could do. [Update] it turns out that installation is a piece of cake, and the download is less than 1Gb … but in general the principle still stands – Docker is a great way to get up and running quickly with something
In this article we’ll take OSA for a spin, looking at some of the functionality and terminology, and then real examples of use with live Twitter data.
To start with, we sign in to Oracle Stream Analytics: Read the complete article here.
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