Azure Stream Analytics vs. IBM Streams (discontinued)

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

Incentivized

Streams is a good fit for situations requiring low end-to-end latency, have complex real-time analytical processing needs on large fast data, or where the reduction of operational costs is important. However, it is very much a data-in-motion technology and not well suited for situations such as some forms of machine learning where the entire historical data set needs to be operated on. Note that it's fairly common to use Streams to perform online scoring using models that were trained offline using other technologies.

Incentivized

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

Incentivized

• Query analysis of real time streaming data
• Filter out events based on time windows
• Scalability for large scale data, production tested

Incentivized

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

Incentivized

• Documentation could be more extensive, with more examples, although overall this is not too bad compared to some of the alternative solutions.
• Seems expensive to use in production.

Incentivized

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

Incentivized

We are using Spark streaming as well as Storm for streaming options. Currently streams provides a better way of building applications easier faster and run efficiently. Also like the flexibility it provides with both us and SPL.

Incentivized

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

Incentivized

• Ability to do more with less
• Admins and data analyst can now focus on more thinking tasks
• No negative impacts yet

Incentivized