Apache Spark Streaming vs. IBM Confluent

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.

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Very reliable product and help with streaming messages without fail which is a significant confidence booster as an SRE leader. Would recommend the product considering the fact that now it is backed by IBM in addition to all IBM Confluent was doing well before the acquisition. Has been a game changer for our organization

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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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• It works really well in providing data context for our customers.
• It is helpful in real time data streaming.
• It is really wonderful tool for doing data validations and massaging the data.

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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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• Iit needs to improve in AI agents usage.
• It needs to be more user friendly.
• It is too complicated for beginners.

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Kafka and Schema Registry are the rock solid fully managed offerings from IBM Confluent Cloud. Other than that the rest of the offerings do not work as expected.

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The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.

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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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For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.

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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.

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Independently Invited by TrustRadius

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• IBM Confluent helps us get data moving from system to system at near real time.
• IBM Confluent makes pushing data into a data lake easy - analytics, targeted deals, etc
• The RBAC and ACL setup helps us govern and secure our data, even between teams/applications.

Incentivized

Invited by TrustRadius (TR) on Vendor's Behalf

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