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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Boomi
Score 8.9 out of 10
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Boomi is a cloud-based, on-premise, or hybrid integration platform. It offers a low-code/no-code
interface with the capacity for API and EDI connections for integrating with external organizations and
systems, as well as compliance with data protection regulations.
$550
per month
Pricing
Apache Kafka
Boomi
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Boomi
$550
per month
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Apache Kafka
Boomi
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No
Free/Freemium Version
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No
Premium Consulting/Integration Services
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No
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No setup fee
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Community Pulse
Apache Kafka
Boomi
Features
Apache Kafka
Boomi
Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).
Dell Boomi is well-suited as a middleware to talk between systems. I am a personal fan of Dell's products and I enjoy this service. Dell Boomi is very easy to use, even to the less technically-inclined user. It is less appropriate to use in one system alone, but users would still find it functional in one system alone because it helps manage the system's metadata and allows that system to be integrated with other systems in the future.
Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.
The Kafka Tool is a community-made Java application that looks and feels from the past century.
Logging can be confusing. This certainly shows when we have to do troubleshooting.
Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.
More from a development perspective. It is always difficult to use the properties features. It takes a while to understand how the data/variables can be used across an integration.
Dell Boomi should also invest more on API Management and not just seen as a ETL,ESB tool.
Should roll out features more often based on users reviews.
Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.
Dell Boomi has provided us with the ability to connect our campus together using our various existing platforms. There are many supported features and have yet to run into something that we cannot do. Its user interface is very intuitive which would allow users to begin developing fairly easily. There is a myriad of resources available
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
First of all, as a service (cloud service), we don't need to care about server maintenance any more, no worries about incorrect configurations, about down time. Second, for maintaining, we don't need a large team to do it. And of course, using Dell Boomi instead of manual transaction could help us to avoid people mistake while inputting data or doing transactions.
I would rate Dell Boomi as highly performant. We have used it for 4+ years and have not had any major issues with availability or speed. We also have not observed performance degradation when connecting it with other software solutions.
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.
Boomi support was responsive and knowledgable, however being a closed cloud service, it doesn't have good community support. We found the learning curve to be steep and there aren't avenues like google, forums, or blogs that provide community driven insight into the product or how to go about designing solutions using the tool
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 scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.
MuleSoft and Boomi are built on totally different frameworks. Mulesoft is an API-led network whereas Boomi is an ETL middleware approach. In easy words, Mulesoft can connect to several apps and business units, can create and reuse multiple components. Boomi offers only on-premise and on-cloud integration solutions whereas MuleSoft is capable of providing on-premise, on-cloud as well as hybrid solutions. We can say that MuleSoft is much more flexible and offers multiple integration solutions.
Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.
ROI was flat. Coding directly in apps we were knowledgable in would have been faster, but maintenance higher.
It brought good process to the integration team that was new, however I wouldn't say it was a huge gain monetarily. It is a valid path out of many paths for integration, but doesn't rule them all.
