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.
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IBM Confluent
Score8.6 out of 10
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IBM Confluent helps enterprises stream, connect, process, govern and serve real-time data across hybrid environments.
$385
per month
Pricing
Apache Kafka
IBM Confluent
Editions & Modules
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Basic
$0
Standard
Starting at ~$385
per month
Enterprise
Starting at ~$1,150
per month
Offerings
Pricing Offerings
Apache Kafka
IBM Confluent
Free Trial
No
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Confluent monthly bills are based upon resource consumption, i.e., you are only charged for the resources you use when you actually use them:
Stream: Kafka clusters are billed for eCKUs/CKUs ($/hour), networking ($/GB), and storage ($/GB-hour).
Connect: Use of connectors is billed based on throughput ($/GB) and a task base price ($/task/hour).
Process: Use of stream processing with Confluent Cloud for Apache Flink is calculated based on CFUs ($/minute).
Govern: Use of Stream Governance is billed based on environment ($/hour).
Confluent storage and throughput is calculated in binary gigabytes (GB), where 1 GB is 2^30 bytes. This unit of measurement is also known as a gibibyte (GiB). Please also note that all prices are stated in United States Dollars unless specifically stated otherwise.
All billing computations are conducted in Coordinated Universal Time (UTC).
Confluent Cloud is still based on Apache Kafka but it has a subscription fee so, from a long term perspective, it is wiser to deploy your own Kafka instance that spans public and private cloud. Amazon Kinesis, Google Cloud Pub/Sub do not do well for a very number of messages …
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
IBM
Verified User
Chose IBM Confluent
Confluent coming from the main contributors to the open source product
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
Verified User
Chose IBM Confluent
IBM Confluent offers a great suite of tools that others don't. Innovation is it's edge, but there are cases where opensource wins - Flink for example.
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
Verified User
Chose IBM Confluent
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 …
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 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.
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
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
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).
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
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
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
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
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 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
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
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.
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
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.
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
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.
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
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.
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
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.
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
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.
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
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
