Both Kafka and Kinesis are often utilized as an integration system in enterprise environments similar to traditional message pub/sub systems. Cross-replication is the idea of syncing data across logical or physical data centers. In this article, I will compare Apache Kafka and AWS Kinesis. Your email address will not be published. Required fields are marked *. Key technical components in the comparisons include ordering, retention period (i.e. With Kinesis – as a managed-service,  Amazon itself takes care of the high-availability of the system so these are less likely to occur. An interesting aspect of Kafka and Kinesis lately is the use of stream processing. Additionally, Apache Kafka … Kafka vs Amazon Kinesis – How do they compare? The Consumer – such as a custom application, Apache hadoop, Apache Storm running on Amazon EC2, an Amazon Kinesis Data Firehose delivery stream, or Amazon Simple Storage Service S3 – processes the data in real time. The Kafka Cluster is made up of multiple Kafka Brokers (nodes in a cluster). The Kafka Cluster consists of many Kafka Brokers on many servers. Then, in stage 3, the data is published to new topics for further consumption or follow-up processing during a later stage. [Kafka] [Kinesis] 6 8. Apache Kafka is an open-source stream-processing software developed by LinkedIn (and later donated to Apache) to effectively manage their growing data and switch to real-time processing from batch-processing. Kinesis is known to be reliable, and easy to operate. Alternatively, If you are looking for a managed solution or you do not have time or expertise and budget at the moment to setup and take care of distributed infrastructure, and you only want to focus on your application, you might lean towards Amazon Kinesis. The number of shards is configurable, however most of the maintenance and configurations is hidden from the user. With Kinesis data can be analyzed by lambda before it gets sent to S3 or RedShift. And as it’s in AWS, it’s production-worthy from the start. In contrast, Amazon Kinesis is a managed service and does not give a free hand for system configuration. So, if you can live with vendor-lockin and limited scalability, latency, SLAs and cost, then it might be the right choice for you. It works  on the principle that there are no upfront costs for setting-up but amount to be paid depends upon the rendered services. In the last post, we compared Apache Kafka and AWS Kinesis Data Streams . Apache Kafka was started as a general-purpose publish and subscribe messaging system and eventually evolved as a fully developed horizontally scalable, fault-tolerant, and highly performant streaming platform. The key advantage of AWS Kinesis is its deep integration into AWS ecosystem. Ongoing ops (human costs) It also might be worth adding that there can be a big difference between the ongoing burden of running your own infrastructure vs. paying AWS … If you don’t have need for scale, strict ordering, hybrid cloud architectures, exactly-once semantics, it can be a perfectly fine choice. The ordering of a product shipping event compared to available product inventory matters. For high availability, Kafka  needs to be configured to recover from failures as soon as possible. Apache Kafka and Amazon Kinesis both provide robust features, but they also have a few limitations. Since this original post, AWS has released MSK. Common use cases include website activity tracking for real-time monitoring, recommendations, etc. Kinesis will take you a couple of hours max. As briefly mentioned above, stream processing between the two options appears to be quite different. Chant it with me now, Your email address will not be published. Cross-replication is not mandatory, and you should consider doing so only if you need it. Moreover, the Kinesis costs are reduced normally with time automatically based on how much your workload is typical to the Amazon. Keep an eye on https://confluent.io. As long as a really good monitoring system is in place for Kafka that is capable of on-time alerting of any failures and a 24/7 team of DevOps taking care of potential failures and recovery, there is a less risk of incidence. Resources for Data Engineers and Data Architects. However, monitoring, scaling, managing and maintaining servers, software, and security of the clusters would still create IT overhead (There are also fully managed services offered by Confluent as well as Amazon Managed Kafka). 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