Likelihood to Recommend
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.
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End-to-end encryption is an amazing feature because we use IoT to connect to various devices in order to gather data/ stats in real-time. We're able to publish solutions with ease and at a faster rate because of AWS IoT Core. However, its inability to interact with other IoT tools is a big con that I would like them to improve upon.
Read full review Pros 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). Read full review AWS IoT Core integrates power analytics and an AI solution for sensor data processing. Great protocol support including HTTPs and MQTT required to connect resource constraint IoT devices to cloud High security standards during data transmission Scalability and a great user community Read full review Cons 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 Read full review Ability to integrate with Ethercalc software solution Ability to introduce NFC toolchains and systems which could be utilized in iot modular gateway solutions like drones Ability to reduce latency as the solution scales is very much needed Hybrid solution which can integrate other existing iot solutions into AWS iot core Read full review Likelihood to Renew
Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet
Read full review Usability
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
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I give AWS IoT Core's overall usability this rating because it is very easy to use and is enjoyed by all of our staff. The only problem is that it sometimes glitches and it freezes a lot. So overall, the usability of AWS IoT Core is very good, and we will continue to use it.
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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.
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It covers all the aspects of IoT services required for an IoT company. It supports all the industry-wide protocols for secure data transmission and integrates powerful AL and ML technology for data analytics. For data storage, Amazon S3 is a great solution. Strong tech support and user community. Since it is widely used as compared to other products, there is an abundance of training and learning material on the web.
Read full review Alternatives Considered
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.
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Azure IoT service provides more or less the same services as compared to AWS IoT core, however the costing of AWS lead us to continued usage of IoT core over Azure IoT services. Also, considering our existing technology stack is on AWS, it was a natural selection for better integration and ease of use.
Read full review Return on Investment 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. Read full review Gives confidence to prototype iot solutions across lots of devices in little time Ability to meet requirements for managing a variety of communication protocols Learning iot analytics and visualization for faster insights enablement Onboarding devices and managing quickly without building inhouse solutions Read full review ScreenShots