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
MariaDB Platform
Score 9.5 out of 10
N/A
MariaDB is an open-source relational database made by the original developers of MySQL, supported by the MariaDB Foundation and a community of developers. The community states recent additional capabilities as including clustering with Galera Cluster 4, compatibility with Oracle Database, and Temporal Data Tables, allowing one to query the data as it stood at any point in the past.
N/A
RabbitMQ
Score 9.0 out of 10
N/A
RabbitMQ, an open source message broker, is part of Pivotal Software, a VMware company acquired in 2019, and supports message queue, multiple messaging protocols, and more.
RabbitMQ is available open source, however VMware also offers a range of commercial services for RabbitMQ; these are available as part of the Pivotal App Suite.
N/A
Pricing
Apache Kafka
MariaDB Platform
RabbitMQ
Editions & Modules
No answers on this topic
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache Kafka
MariaDB Platform
RabbitMQ
Free Trial
No
Yes
No
Free/Freemium Version
No
Yes
No
Premium Consulting/Integration Services
No
Yes
No
Entry-level Setup Fee
No setup fee
Optional
No setup fee
Additional Details
—
—
—
More Pricing Information
Community Pulse
Apache Kafka
MariaDB Platform
RabbitMQ
Considered Multiple Products
Apache Kafka
Verified User
Engineer
Chose Apache Kafka
I would only use RabbitMQ over Kafka when you need to have delay queues or tons of small topics/queues around. I don't know too much about Pulsar - currently evaluating it - but it's supposed to have the same or better throughput while allowing for tons of queues. Stay tuned - I …
Kafka is not a real messaging broker implementation as RabbitMQ or TIBCO EMS/JMS are. Although it can be used as messaging, we like the idea behind the Kafka (data isn't "passing by," instead it remains centra, so the client can revisit the data if necessary). This also …
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data …
We really needed to get away from using a SQL database to act as a queue for processing records, so a new solution was needed. Kafka is a leading software application initially designed for queuing messages which is essentially what we were looking for. It has a great user …
All stack tech helps our app and system. These technologies allow us to have the data available faster between different regions (due to our particular configuration) and thus the data and processing load of each system is lower. This allows the systems to be used more …
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 …
It did not always compete against these technologies. Most of the time, it was complementing these databases for certain use cases to help provide a much more complete database. This makes more users want to use it to explore new solutions that help users. This is our target …
It is very easy to use as it has a simple function to connect and use RabbitMQ. It is having Fast Learning curve, Any newbies can learn it in a week or month. It is having proper documentation, we are able to find all the details about its functionality and usage of it. The …
Honestly, though we're still trying out Kafka and Pulsar, I'd go with them for message broker and as traffic buffers. We are only still using RabbitMQ because it's hard to transition off after writing tons of code custom-built for RabbitMQ. Kafka is better because it's way more …
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.
It is highly recommended that if you have microservices architecture and if you want to solve 2 phase commit issue, you should use RabbitMQ for communication between microservices. It is a quick and reliable mode of communication between microservices. It is also helpful if you want to implement a job and worker mechanism. You can push the jobs into RabbitMQ and that will be sent to the consumer. It is highly reliable so you won't miss any jobs and you can also implement a retry of jobs with the dead letter queue feature. It will be also helpful in time-consuming API. You can put time-consuming items into a queue so they will be processed later and your API will be quick.
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).
Simpler learning curve. MariaDB is a cleaner, simpler system that is (IMO) easier to learn and easier to manage effectively than many other database systems.
Lower hardware requirements. After migrating to MariaDB from another database software system, we find that our hardware needs have substantially decreased.
MariaDB support is very responsive. It's like they actually care. On the few occasions we've run into technical issues, support has always come through with what we needed. Once it was showing me a relatively new feature the server supported that I wasn't aware of, that, once I was able to properly make use of it helped me resolve a serious production performance issue.
Architectural flexibility. As an example, the ready availability of synchronous (Galera) versus asynchronous replication schemes without being locked into one of the other by enormous technical complexity or punitive licensing, allows the customer to find what really works best for their needs.
What RabbitMQ does well is what it's advertised to do. It is good at providing lots of high volume, high availability queue. We've seen it handle upwards of 10 million messages in its queues, spread out over 200 queues before its publish/consume rates dipped. So yeah, it can definitely handle a lot of messages and a lot of queues. Depending on the size of the machine RabbitMQ is running on, I'm sure it can handle more.
Decent number of plugins! Want a plugin that gives you an interface to view all the queues and see their publish/consume rates? Yes, there's one for that. Want a plugin to "shovel" messages from one queue to another in an emergency? Check. Want a plugin that does extra logging for all the messages received? Got you covered!
Lots of configuration possibilities. We've tuned over 100 settings over the past year to get the performance and reliability just right. This could be a downside though--it's pretty confusing and some settings were hard to understand.
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
Driver Support - Some third party applications use database drivers that cause unexplained slowness with MariaDB. This can be worked around by using the MySQL drivers, but it's not clear what causes the problem in the first place.
Support - While online communities are helpful in diagnosing problems, there isn't as much professional documentation/support available for MariaDB as some of the other major database options.
Data Visualization - It would be helpful if there were more built in options for analyzing statistics and generating reports.
It breaks communication if we don't acknowledge early. In some cases our work items are time consuming that will take a time and in that scenario we are getting errors that RabbitMQ broke the channel. It will be good if RabbitMQ provides two acknowledgements, one is for that it has been received at client side and second ack is client is completed the processing part.
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
MariaDB is very usable and stable to be used in production settings as an alternative to MySQL. The shortcomings of SQL are present but well understood in the community, and if the decision were to be made again, I would choose MariaDB over MySQL on future projects.
RabbitMQ is very easy to configure for all supported languages (Python, Java, etc.). I have personally used it on Raspberry Pi devices via a Flask Python API as well as in Java applications. I was able to learn it quickly and now have full mastery of it. I highly recommend it for any IoT project.
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.
We have launched several inquiries to MariaDB support and they have always responded very quickly and have not been tutoring for the duration of the incident/problem.
Likewise, they want to hold constant meetings with the client to get their opinion as well as how they can help.
I see a very human support and concerned about the customer.
I gave it a 10 but we do not have a support contract with any company for RabbitMQ so there is no official support in that regard. However, there is a community and questions asked on StackOverflow or any other major question and answer site will usually get a response.
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.
MariaDB stacks up the the competition just fine. Due to is ture open source nature we do not have to worry about licencing and spending money on nothing. Moreover, MariaDB does everything that we need to get done. We can run data that is a million rows or many smaller projects on the same environment with little overhead. One of the best features that MariaDB has is the ability of backup or dump data to standard text sql statements. That was one of the reasons why we choose MariaDb because it makes backups or transferring data a snap
RabbitMQ has a few advantages over Azure Service Bus 1) RMQ handles substantially larger files - ASB tops out at 100MB, we use RabbitMQfor files over 200MB 2) RabbitMQ can be easily setup on prem - Azure Service Bus is cloud only 3) RabbitMQ exchanges are easier to configure over ASB subscriptions ASB has a few advantages too 1) Cloud based - just a few mouse clicks and you're up and running
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.
Positive: we don't need to keep way too many backend machines around to deal with bursts because RabbitMQ can absorb and buffer bursts long enough to let an understaffed set of backend services to catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines around.
Negative: we've got many angry customers due to queues suddenly disappearing and dropping our messages when we try to publish to them afterward. Ideally, RabbitMQ should warn the user when queues expire due to inactivity but it doesn't, and due to our own bugs we've lost a lot of customer data as a result.
Positive: makes decoupling the web and API services from the deeper backend services easier by providing queues as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
