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When we raise a ticket or have an issue, the support team is responsive.
Overall, it is good, but there is some room for improvement when it comes to response time and overall competence.
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.
Whenever the number of requests increases, the system automatically scales up to the target we have set and scales down once the requests are resolved.
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.
AWS Lambda needs to improve cold start time.
Regarding scaling, we can add up to 1,000 execution environments for every 10 seconds per function, per region.
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.
Automatic scaling is a valuable feature. When the number of requests increases, the system automatically scales up to the target we have set and scales down once the requests are resolved.
As it is serverless, AWS Lambda has more scope for building scalable architectures.
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 | Mindshare (%) |
|---|---|
| AWS Lambda | 14.2% |
| Amazon EC2 | 13.6% |
| AWS Fargate | 10.4% |
| Other | 61.800000000000004% |
| Product | Mindshare (%) |
|---|---|
| Azure Stream Analytics | 6.1% |
| Apache Flink | 8.9% |
| Databricks | 8.1% |
| Other | 76.9% |
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 15 |
| Large Enterprise | 44 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 3 |
| Large Enterprise | 18 |
AWS Lambda offers a serverless architecture that facilitates seamless integration with other AWS services, providing rapid scalability and cost efficiency. It supports event-driven computing and multiple programming languages, allowing for automatic scaling and enhanced performance.
AWS Lambda is favored for its ease of integration with AWS services like S3, API Gateway, and DynamoDB, ensuring efficient application and scaling. It supports rapid deployment with low coding requirements, parallelism, and event-triggered execution, making it suitable for event-driven processes, API services, data processing, and backend functions. While improvements in integration with external services, execution time limits, cold start latency, and support for more programming languages are needed, its price and monitoring tools could be optimized further. Users desire simplified deployments and improved documentation, especially for high-demand applications.
What are AWS Lambda's most valuable features?AWS Lambda is widely used in industries like IoT, finance, and education for its ability to handle image processing, authentication, and real-time notifications. Its flexibility and integration capabilities make it suitable for integrating CI/CD pipelines, automating workloads, and supporting event-driven processes across diverse industry applications.
Azure Stream Analytics offers real-time data processing with seamless IoT hub integration and user-friendly setup. It efficiently manages data streams and supports Azure services, SQL Server, and Cosmos DB.
Azure Stream Analytics specializes in real-time data analytics, easily integrating with Microsoft technologies. It enables swift deployment, monitoring, and high-performance data streaming. Though praised for its powerful SQL language and machine learning capabilities, users face challenges with historical analysis, pricing clarity, debugging, and data connection outside Azure. Limited real-time data joining, query customization, and complex data handling are noted alongside needs for improved technical support, job monitoring, and trial periods.
What are the key features of Azure Stream Analytics?Azure Stream Analytics is leveraged in industries for real-time IoT data processing, predictive analytics, and accident prevention in logistics. It supports telemetry data processing for applications like predictive maintenance and integrates with Power BI for enhanced data visualization, aligning with Azure's IoT infrastructure.
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