Azure Stream Analytics and Amazon Kinesis are competitive real-time data streaming solutions. Azure Stream Analytics is considered to have an advantage due to its pricing and integration capabilities with other Azure services.
Features: Azure Stream Analytics offers seamless integration with Azure services such as Azure Blob Storage and Power BI, supports SQL-based queries, and boasts robust IoT capabilities. Amazon Kinesis provides excellent real-time streaming capabilities, integrates well with AWS Lambda for data processing, and offers a user-friendly interface for data analytics.
Room for Improvement: Azure Stream Analytics is often criticized for being too Azure-centric, causing issues with non-Azure platforms, and for needing better Power BI integration support. Amazon Kinesis could improve in areas such as complex sharding, its machine learning capabilities, and data retention periods.
Ease of Deployment and Customer Service: Azure Stream Analytics is praised for robust support and efficient deployment within the Azure ecosystem. Amazon Kinesis is noted for its ease of initiation but relies heavily on community forums and online documentation for support.
Pricing and ROI: Azure Stream Analytics is cost-effective for small-scale needs, though its pricing model can be confusing. High scalability can increase costs for large enterprises. Amazon Kinesis offers scalability and is cost-efficient compared to non-hosted solutions such as Kafka, but costs can rise with increased data volumes.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
We receive prompt support from AWS solution architects or TAMs.
There is a big communication gap due to lack of understanding of local scenarios and language barriers.
They've managed to answer all my questions and provide help in a timely manner.
The support on critical issues depends on the level of subscription that you have with Microsoft itself.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
I would rate the scalability of Amazon Kinesis as a nine.
Maintenance requires a couple of people, however, it's not a full-time endeavor.
This is crucial for applications demanding constant monitoring, such as healthcare or financial services.
Azure Stream Analytics is scalable, and I would rate it seven out of ten.
I would rate the stability of Amazon Kinesis as high, giving it a 10.
They require significant effort and fine-tuning to function effectively.
For example, Azure Stream Analytics processes more data every second, which is why it's recommended for real-time streaming.
There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
A cost comparison between products is also not straightforward.
There's setup time required to get it integrated with different services such as Power BI, so it's not a straight out-of-the-box configuration.
Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no-code platforms to enhance performance.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
Choosing between pay-as-you-go or enterprise models can affect pricing, and depending on data volume, charges might increase substantially.
From my point of view, it should be cheaper now, considering the years since its release.
We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
Amazon Kinesis integrates easily with the AWS environment.
It's very accurate and uses existing technologies in terms of writing queries, utilizing standard query languages such as SQL, Spark, and others to provide information.
Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond.
It is quite easy for my technicians to understand, and the learning curve is not steep.
| Product | Market Share (%) |
|---|---|
| Amazon Kinesis | 5.6% |
| Azure Stream Analytics | 6.2% |
| Other | 88.2% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 10 |
| Large Enterprise | 9 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 3 |
| Large Enterprise | 18 |
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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