Amazon Kinesis vs. Azure Stream Analytics vs. Sybase Event Stream Processor (discontinued)

Amazon Kinesis is a great replacement for Kafka and it works better whenever the components of the solution are AWS based. Best if extended fan-out is not required, but still price-performance ratio is very good for simplifying maintenance.

I would go with a different option if the systems to be connected are legacy, for instance in the case of traditional messaging clients.

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Data enrichment is effectively done in stream analytics also checking the values with different functionality like windowing and group by clause is effectively working.

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These complex event processing platforms have the ability to process to multiple streams of data with low latency and ability to scale up well. It provides the ability to analyze streaming data real time and establish/investigate patterns with this streaming data and also deals with high-speed data very well.

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• Processing huge loads of data
• Integrating well with IoT Platform on Amazon
• Integration with overall AWS Ecosystem
• Scalability

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• Routing of data from multiple inputs to multiple output
• You create your own user define function.
• Intermediate query is working very effectively.

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• Ability to capture data and investigate data for pattern fairly quickly
• Integrates well with Sybase database
• Ability to do data modeling

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• Not a queue system, so little visibility into "backlog" if there is any
• Confusing terminology to make sure events aren't missed
• Sometimes didn't seem to trigger Lambda functions, or dropped events when a lot came in

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• Code competency is not that much effective
• Ml models can't be integrated with stream analytics

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• Configuring data streams could be made more intuitive to users
• Sometimes debugger tools are very slow

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The documentation was confusing and lacked examples. The streams suddenly stopped working with no explanation and there was no information in the logs. All these were more difficult when dealing with enhanced fan-out. In fact, we were about to abort the usage of Kinesis due to a misunderstanding with enhanced fan-out.

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The main benefit was around set up - incredibly easy to just start using Kinesis. Kinesis is a real-time data processing platform, while Kafka is more of a message queue system. If you only need a message queue from a limited source, Kafka may do the job. More complex use cases, with low latency, higher volume of data, real time decisions and integration with multiple sources and destination at a decent price, Kinesis is better.

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Azure Stream Analytics is easy to implement and also to integrate compare to other services like iot analytics

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IBM WebSphere business elements which was more complex to set up and also more expensive especially in a large SAP environment, We were also looking to get ramped up with the product with minimal learning curve especially with good documentation available to get started with the product as well as the ability to deploy and maintain with relative ease on an ongoing basis.

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• Caused us to need to re-engineer some basic re-try logic
• Caused us to drop some content without knowing it
• Made monitoring much more difficult
• We eventually switched back to SQS because Kinesis is not the same as a Queue system

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• Very nice roi while using it.
• Multiple integration is the best functionality

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• A positive impact on real-time analysis and patterns from streaming data
• Good speed
• Good documentation for setup
• No negative impact

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