Azure Stream Analytics and Amazon MSK are products in the streaming data and analytics category. Azure Stream Analytics has the upper hand with more cost-effective solutions and easier integration with its ecosystem, whereas Amazon MSK offers superior features through its extensive Kafka support.
Features: Azure Stream Analytics is notable for its seamless integration with Azure services, offering valuable features like real-time analytics that can be sent directly to Power BI and a user-friendly interface that simplifies complex data operations. It also supports robust integration with Azure IoT hub and Blob storage. On the other hand, Amazon MSK provides strong scalability and Kafka support, making it ideal for companies that need a dependable streaming data solution. Its ability to integrate well with AWS services enhances its value, especially for companies already within the AWS ecosystem.
Room for Improvement: Azure Stream Analytics could enhance its features by improving integration capabilities with non-Azure platforms, expanding its real-time analytics functionalities, and optimizing large-scale deployment performance. Amazon MSK would benefit from streamlining its initial setup, providing better step-by-step deployment guides, and improving cost-effectiveness for smaller workloads.
Ease of Deployment and Customer Service: Azure Stream Analytics is known for its straightforward setup and excellent customer service, ensuring prompt and accessible support. Amazon MSK, while offering efficient customer service, involves a more complex deployment process that could be simplified.
Pricing and ROI: Azure Stream Analytics is cost-effective, providing a high ROI for businesses with its competitive pricing structure. Amazon MSK entails a higher setup cost but justifies this with robust long-term features and scalability, making it worth the investment for substantial data streaming requirements.
Amazon's support is excellent.
There is a big communication gap due to lack of understanding of local scenarios and language barriers.
Any time I needed assistance, they were helpful.
The functionality for scaling comes out of the box and is very effective.
Maintenance requires a couple of people, however, it's not a full-time endeavor.
Azure Stream Analytics is scalable, and I would rate it seven out of ten.
They require significant effort and fine-tuning to function effectively.
The increase in cloud costs by 50% to 60% does not justify the savings.
A cost comparison between products is also not straightforward.
There is a lack of technical support from Microsoft's local office, particularly in Taiwan.
Once we started using Kafka, our cloud costs rose by 50% to 60%.
The Azure solution is better now, and competitors, even within Microsoft, may offer solutions that could make it cheaper.
We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud.
The scalability and usability are quite remarkable.
Clients can choose and subscribe to the service items they need, making it more flexible than IBM solutions, especially in data analytics or data governance.
The native connectors and integration with other Microsoft products.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.
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
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
