Aerospike vs. Apache Kafka

We were developing an advertisement time auction application, where we had to store the client's personal details, advertisement-related details, location, and many other details. Moreover, we required a promotion, cookies, and a few more details from the front end. All this information is heavy in terms of size and cannot be lost if the server crash. So, we required an extremely fast disk database with high scalability and low throughput.

Incentivized

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.

• Low latency
• Stable
• Highly configurable
• increasing features

Incentivized

• 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).

Incentivized

• Load balancing per network segments.
• Reduction in price.
• Cross datacenter replication usage isn't so straightforward. Sometimes cross dc replication can have issues of bad data..

Incentivized

• 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

If money isn't an issue, and you're not on the cloud, then I'd go with Aerospike. If you're the cloud ie, aws or azure, then i'd stick with dynamoDB or Cosmos then. Aerospike is definitely not something you want to put into the cloud. It doesn't work well w/ cross regions. If cross DC, you'll have to write some stuff for data integrity checks.

Incentivized

Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet

Incentivized

We were in dilemma in deciding the database and it was the first time we were using Aerospike. Eventually, everything went as expected and resolved the client's requirement along with positive feedback and appreciation

Incentivized

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

Incentivized

You pay for the level of support.

Incentivized

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.

Incentivized

Aerospike is much more performant than MongoDB, however there is much greater community adoption and support for mongo

Incentivized

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.

Incentivized

• increased response time
• minimal managerial resource required
• developer can start using with shallow learning curve

Incentivized

• 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.

Incentivized