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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Zabbix
Score 8.7 out of 10
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Zabbix is an open-source network performance monitoring software. It includes prebuilt official and community-developed templates for integrating with networks, applications, and endpoints, and can automate some monitoring processes.
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.
Zabbix is great for monitoring your servers and seeing alerts when the system uses too much CPU or memory. This allowed the system Engineer to be proactive and add resources to these systems to avoid interrupting the services. Especially servers running operations applications and services. This is one of the best usages for Zabbix.
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).
Collecting hardware data - CPU, Memory, Network, and Disk Metrics are collected and reported on.
Flexible design - It is very easy to build out even very large environments via the templating system. You can also start where you are - network monitoring, server monitoring, etc. and then build it out from there as time and resources permit.
Provides a "plugin architecture" (via XML templates) to allow end users to extend it to monitor all kinds of equipment, software, or other metrics that are not already added into the software already.
Very complete documentation. Almost every aspect of Zabbix has been documented and reported on.
Cost - Zabbix is FOSS software and always free. Support is reasonably priced and readily available.
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
It is free. It didn't cost anything to implement (other than my time and the cost incurred for it) and it is filling a badly needed gap in our IT infrastructure. Support is available if we have issues and can be done annually or paid for on a per incident basis as needed. Expansion, updates, and all other future lifecycle activities are likewise free of cost, so as long as someone is able to implement/maintain the software (and the OSS project is maintained) then I imagine the company will never leave it.
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
I think every organization, especially the IT department, needs a tool like this. I know of another product like Zabbix that gives a similar or the same solution, but its range makes it very useful. You can see almost all the device info in one place: disk usage, disk space, network usage, etc.
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.
The setup is the most time-consuming portion of using zabbix. It takes a lot of effort to shape it into a usable format and even then it can get very messy. It's not exactly intuitive and as mentioned the UI seems a bit antiquated. If I was to roll out a monitoring solution from scratch, I'd probably look for alternatives which are easier to use and maintain.
We are a mainly Windows environment, so it would be useful if we could have used Active Directory to deploy agents. As of version 4.2, Zabbix has announced a new agent MSI file to allow exactly that. Unfortunately, we didn't have that option. Also, for Linux and MAC deployments, there is no simple way to deploy that. Using remote scripts you may be able to create something, but most places will opt for either SNMP (agentless) or manual installation of agents to add to Zabbix. A way of deploying agents via discovery would go a long way to helping in the adoption of the tool.
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.
We're using the Solarwinds suite as our global monitoring standard, but it is very complex and its licensing model makes it difficult to monitor a wide range of technologies. So, we're using Zabbix as a complement on our monitoring process. Zabbix is a way more flexible and has free integrations to a wide range of technologies. It is also more 'user friendly' and easy to manage.
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.
