Analytics is the key to making your data useful and supporting decision making. Today I’m excited to talk about Azure Stream Analytics. Azure Stream Analytics is an event processing engine that allows you to capture and examine high volumes of data from all kinds of connections, like devices, websites and social media feeds.
You can examine those data streams and it allows you to trigger things like alerts, as well as take action with reporting or storage. So, whether you want to report on it with Power BI or store the data for down the road, you have these options. Stream analytics is used a lot with IoT or streaming feeds through social media, where people want to keep an eye on what’s happening with the data.
Here’s how it works. It starts with a data source such as Event Hub, IoT Hub or Azure Blob Storage, and it uses SQL-like query language that allows transformation on the fly. It helps you process operations like filtering, sorting, aggregating and joining the data together to make it more useable—turning data into information.
From there, when you identify the data that you want/need to use, you can then send that data downstream to be sent to a queue for triggering workflows or further processing of the data. You can also send that data to Power BI for real-time visualization. For example, let’s say you’re looking at a data quality stream and you want to pull certain key words out of Twitter to see how they’re used and watch how that’s being done. By connecting to the Twitter API, you can capture that data, stream it, and then report from it with a Power BI report.
Of course, the other option is to archive it for further processing down the road if you want to do something with that data.
This was designed to be easy to use and spin up. It has source and sync integration and an easy to use declarative SQL query-like language. Also, it’s a managed service so it’s pay-as-you-use, as with many Azure services. There’s no need to buy hardware or software up front. And it has an enterprise grade service level agreement so it’s robust, reliable and you can have multi-locations.
Another big positive is it’s in-memory processing with multi-node capabilities offers tremendous scalability and performance benefits. Plus, unlike on prem solutions it can be fairly elastic, so you can buy nodes as you need them to process more data and you can bring them back down when you’re not using them.
There are a lot of cool things being done with stream analytics and IoT; it’s an exciting time to be in this arena.
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