Apache Kafka vs. Sybase Event Stream Processor (discontinued)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Kafka
Score 8.4 out of 10
N/A
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.N/A
Sybase Event Stream Processor (discontinued)
Score 3.9 out of 10
N/A
Base on the former Aleri product, Sybase Event Stream Processor, from SAP, is currently discontinued and no longer supported, or available for sale.N/A
Pricing
Apache KafkaSybase Event Stream Processor (discontinued)
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
Apache KafkaSybase Event Stream Processor (discontinued)
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache KafkaSybase Event Stream Processor (discontinued)
Top Pros
Top Cons
Best Alternatives
Apache KafkaSybase Event Stream Processor (discontinued)
Small Businesses

No answers on this topic

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Medium-sized Companies
IBM MQ
IBM MQ
Score 9.1 out of 10
Confluent
Confluent
Score 7.3 out of 10
Enterprises
IBM MQ
IBM MQ
Score 9.1 out of 10
Spotfire Streaming
Spotfire Streaming
Score 7.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache KafkaSybase Event Stream Processor (discontinued)
Likelihood to Recommend
8.3
(18 ratings)
7.0
(1 ratings)
Likelihood to Renew
9.0
(2 ratings)
-
(0 ratings)
Usability
10.0
(1 ratings)
-
(0 ratings)
Support Rating
8.4
(4 ratings)
-
(0 ratings)
User Testimonials
Apache KafkaSybase Event Stream Processor (discontinued)
Likelihood to Recommend
Apache
Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.
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Discontinued Products
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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Pros
Apache
  • Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
  • Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
  • Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).
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Discontinued Products
  • 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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Cons
Apache
  • Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
  • Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
  • Learning curve around creation of broker and topics could be simplified
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Discontinued Products
  • Configuring data streams could be made more intuitive to users
  • Sometimes debugger tools are very slow
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Likelihood to Renew
Apache
Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet
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Discontinued Products
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Usability
Apache
Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
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Discontinued Products
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Support Rating
Apache
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
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Discontinued Products
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Alternatives Considered
Apache
I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.
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Discontinued Products
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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Return on Investment
Apache
  • Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
  • Positive: it's scalable so we can develop small and scale for real-world scenarios
  • Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.
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Discontinued Products
  • 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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