Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service.
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.
I would rate how scalable AWS Lambda is a nine on a scale from 1 to 10, where 1 would be the lowest and 10 would be the highest level of scalability.
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.
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.
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.
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.
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 (%) |
|---|---|
| AWS Lambda | 16.6% |
| AWS Batch | 15.6% |
| Apache Spark | 11.4% |
| Other | 56.4% |
| Product | Market Share (%) |
|---|---|
| Azure Stream Analytics | 7.2% |
| Apache Flink | 14.4% |
| Databricks | 11.8% |
| Other | 66.6% |
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 15 |
| Large Enterprise | 42 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 3 |
| Large Enterprise | 18 |
AWS Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running. With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. All you need to do is supply your code in one of the languages that AWS Lambda supports (currently Node.js, Java, C# and Python).
You can use AWS Lambda to run your code in response to events, such as changes to data in an Amazon S3 bucket or an Amazon DynamoDB table; to run your code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs. With these capabilities, you can use Lambda to easily build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB process streaming data stored in Amazon Kinesis, or create your own back end that operates at AWS scale, performance, and security.
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 Compute Service 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.
