Azure Stream Analytics and Amazon Kinesis compete in the data streaming category. Amazon Kinesis has the advantage due to its simplicity and cost-effectiveness for smaller use cases.
Features: Azure Stream Analytics benefits from integration with Azure resources, real-time analytics with Power BI, and IoT hub integration. Amazon Kinesis offers automatic scaling, data transformation capabilities, and ease of management with AWS hosting.
Room for Improvement: Azure Stream Analytics needs clearer pricing, enhanced error logging, and improved Power BI integration. Amazon Kinesis could enhance sharding scalability, make Kinesis Analytics more user-friendly, and improve technical documentation.
Ease of Deployment and Customer Service: Azure Stream Analytics supports various cloud deployments, with varying support experiences. Amazon Kinesis supports public cloud deployment, with straightforward setup but depends on user initiative for problem-solving.
Pricing and ROI: Azure Stream Analytics charges per streaming unit, making it complex and costly at scale, but offers value for those using Azure services. Amazon Kinesis is cost-effective, particularly for smaller projects, with pricing based on usage levels.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.
Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.
Top Benefits
Reviews from Real Users
“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.
“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys
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