Apache Spark Streaming vs. Azure Stream Analytics

Apache Spark Streaming is a tool that we are using for almost a year and is excellent in managing batch processing. It is user-friendly. Using it, we can even process our massive data in fractions of seconds. Its pricing is its other plus point. Only its In-memory processing is its demerit as it occupies a large memory.

Incentivized

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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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• It is amazing in solving complicated transformative logic.
• It is straightforward to program.
• It is a very quick tool.
• It processes large data within a fraction of seconds.

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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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• There must be more documentation.
• It is a profoundly complex tool.
• Its in-memory processing consumes massive memory.

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Independently Invited by TrustRadius

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

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

Apache Spark Streaming stands above all the huge data transformative tools because of its speed of processing which was quite slow in Presto as it takes a lot of our time in the data processing. Spark, comfortably provides integration with Jupyter like notebook environment. and Spark's combination with Jupyter and Python results in enhancing the speed .

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

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

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Cost and time-effective tool for our business.
• We can integrate with Jupyter with many conveniences.
• Its high-speed data processing has proved beneficial for us.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

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

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info