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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Make
Score 9.3 out of 10
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Make (formerly Integromat) automates integration between applications. It features data transformation capabilities within a no-code graphic interface.
The former Integromat was acquired by Celonis in 2020, and the current product Make is a Celonis brand.
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Apache Kafka
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Apache Kafka
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Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
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.
Integromat is the best tool for business automation in my opinion because unlike Zapier it allows us to integrate with any API even if the app is not available which allows us to create automation even with the less known apps that we use or the ones that we built internally for our own company.
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).
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
At this point, it is firmly embedded in the DNA of the business and to give up the ability to automate workflows and create integrations on the fly would be a terrible idea.
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
Make's easy to understand user interface helps you to visualize what's happening at all times. Could use some tweaks around the navigation from a scenario specifically in the folders and back navigations. I can't tell you the amount of time wasted in that area. When you branch, you can't bring a branch back together in the same scenario which is kind of a bummer as well.
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 pricing schema is very attractive, almost 50% lower than the competition. You could start from free and then grow. It has a pretty big library of connections to other apps and services, which really helps you when everything is a mess. Integromat has a really easy-to-use interface. You could do almost everything with fewer than 5 clicks. Scenarios (automation steps to complete a routine) have graphics so you can configure them more easily.
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.
Integromat allows us to do everything we used to do on Zapier but it doesn't limit us to only the popular apps, with Integromat we're integrating custom APIs and we get data from different servers through GET requests and it's exactly what we needed and Zapier couldn't provide it.
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.
