Amazon Kinesis is a streaming analytics suite for data intake from video or other disparate sources and applying analytics for machine learning (ML) and business intelligence.
$0.01
per GB data ingested / consumed
Apache Kafka
Score8.9 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
IBM MQ
Score9 out of 10
N/A
IBM MQ (formerly WebSphere MQ and MQSeries) is messaging middleware.
N/A
Pricing
Amazon Kinesis
Apache Kafka
IBM MQ
Editions & Modules
Amazon Kinesis Video Streams
$0.00850
per GB data ingested / consumed
Amazon Kinesis Data Streams
$0.04
per hour per stream
Amazon Kinesis Data Analytics
$0.11
per hour
Amazon Kinesis Data Firehose
tiered pricing starting at $0.029
per month first 500 TB ingested
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Amazon Kinesis
Apache Kafka
IBM MQ
Free Trial
No
No
Yes
Free/Freemium Version
No
No
Yes
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Amazon Kinesis
Apache Kafka
IBM MQ
Considered Multiple Products
Amazon AWS
Verified User
Chose Amazon Kinesis
The main benefit was around set up - incredibly easy to just start using Kinesis. Kinesis is a real-time data processing platform, while Kafka is more of a message queue system. If you only need a message queue from a limited source, Kafka may do the job. More complex use …
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
Verified User
Chose Apache Kafka
Apache Kafka is open-sourced, scales great has cloud agnostics and performs better than Amazon Kinesis [in my view]. Amazon Kinesis has some limitations and vendor lockin is not something I [like]. With Confluent operators you can easily install it on a kubernetes cluster.
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 Apache Kafka
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
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. TR verified that a representative sample of customers was invited. More Info
Global Technology Centre - Middleware in Information Technology at Produban (Financial Services, 10,001+ employees)
Chose Apache Kafka
Kafka is faster and more scalable, also "free" as opensource (albeit we deploy using a commercial distribution). Infrastructure tends to be cheaper. On the other hand, projects must adapt to Kafka APIs that sometimes change and BAU increases until a major 1.x version comes out …
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 MQ
I've also used Apache Kafka and RabbitMQ. Compared to these, IBM MQ offers superior reliability and transactional integrity, making it a better choice for complex, mission-critical enterprise environments where message delivery and security are paramount. We chose IBM MQ for …
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
Survey Programmer Qualtrics in Information Technology at Course5i (Market Research, 51-200 employees)
Chose IBM MQ
Apache Kafka may be a better option in comparison with IBM MQ its real-time data streaming and large data payload service. It depends upon the specific requirement and meets those needs. MuleSoft any point platform is very easy to connect to various other types of platforms in …
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 MQ
Kafka is renowned for its impressive throughput, fault tolerance, and real-time data streaming capabilities. Nonetheless, IBM MQ remains the preferred choice due to its unwavering commitment to guaranteed delivery and exceptional reliability. Fault-Tolerant Architectures of IBM …
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. TR verified that a representative sample of customers was invited. 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
.NET Engineer in Information Technology at Poslovna Inteligencija (Information Technology & Services, 51-200 employees)
Chose IBM MQ
We found IBM MQ very easy to get started and quick to learn by the new users with a short learning curve and seamlessly integrates with IBM products, and quick to perform self-service analytics and make informed business decisions. IBM MQ is also very straightforward in …
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 MQ
IBM MQ is very stable and a proven product compared to other Messaging platforms available. Performance was better than WSO2 product and also the RabbitMQ. Though Kafka and IBM MQ is not directly comparable, Kafka is more suited for event based systems and also where there is …
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
Amazon Kinesis is a great replacement for Kafka and it works better whenever the components of the solution are AWS based. Best if extended fan-out is not required, but still price-performance ratio is very good for simplifying maintenance.
I would go with a different option if the systems to be connected are legacy, for instance in the case of traditional messaging clients.
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.
In the context of Internet of Things (IoT) applications, IBM MQ plays a pivotal role in managing the substantial data streams emanating from interconnected devices. Its primary function is to guarantee the dependable transmission and processing of data, catering to a diverse range of IoT use cases, including but not limited to smart city initiatives, healthcare monitoring systems, and industrial automation solutions. In the telecommunications sector, IBM MQ is employed for message routing, call detail record (CDR) processing, and network management to ensure real-time data exchange and fault tolerance. When managing the supply chain and logistics, IBM MQ is used to ensure timely and accurate communication between different entities, including suppliers, warehouses, and transportation providers. IBM MQ can be cost-prohibitive for smaller organizations due to licensing and maintenance costs. In such cases, open-source or lightweight messaging solutions may be more appropriate. For scenarios requiring extremely low-latency, real-time data exchange, and high throughput, other messaging technologies, like Apache Kafka, may be more suitable due to their specialized design for such use cases.
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
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
The documentation is very clear,It is understandable and the support helps to configure it in the best way.
Server guidelines make it possible to get the most out of work management. It's broad, we can work with different operating systems, I really recommend using linux.
It is highly compatible with systems, brockers, applications, and data accumulation programs, it is possible to configure everything so that after the installation of programs, they can communicate with each other and then throw data to an external program that accumulates it and represents in clear details of steps to follow and make business decisions.
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
There is limitation on number of svrconn connections you can have to MQ on the mainframe which has been an major issue for us. This has been an issue for us for over 4 years and still no fix although I am aware IBM have been working on a solution over the last year.
When upgrading to MQ V9.3 on our MQ appliances there is no fall-back option. This was the same for MQ V9.2 upgrade from MQ V9.0. For production upgrades this I believe is not acceptable.
AMS is not supplied as part of the standard mainframe MQ licence. You need an extra licence. IBM tell customers how important security and protecting data is yet they still want to charge for this software. The cost of MQ on the mainframe is not cheap so I would expect AMS to be part of the base 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
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
I give it a nine because it has significantly improved my team's data reliability and operational efficiency. Its great security features give us peace of mind, knowing our sensitive data is well protected. While the setup might initially be complex, I believe the long-term benefits far outweigh this hurdle.
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 messages are delivered instantly with this software and it integrates with our technology stack, in terms of availability we only had one failure when we were doing some testing and integration with third parties, the features of this software make it always available and its deployment is easy for the company, it does not generate expenses due to failures
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 documentation was confusing and lacked examples. The streams suddenly stopped working with no explanation and there was no information in the logs. All these were more difficult when dealing with enhanced fan-out. In fact, we were about to abort the usage of Kinesis due to a misunderstanding with enhanced fan-out.
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
There are very specific things that must be elevated to more specialized areas of support, but the common support is very agile when receiving questions or when we leave concerns in real time. I recommend the support of the program in this regard.
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 main benefit was around set up - incredibly easy to just start using Kinesis. Kinesis is a real-time data processing platform, while Kafka is more of a message queue system. If you only need a message queue from a limited source, Kafka may do the job. More complex use cases, with low latency, higher volume of data, real time decisions and integration with multiple sources and destination at a decent price, Kinesis is better.
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
We found IBM MQ very easy to get started and quick to learn by the new users with a short learning curve and seamlessly integrates with IBM products, and quick to perform self-service analytics and make informed business decisions. IBM MQ is also very straightforward in creating simple and best reports, which are very profitable and productive.
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
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. TR verified that a representative sample of customers was invited. More Info
