Apache Kafka Valuable Features

Fbc6d212 93a9 4393 a7b1 de1e33e98003 avatar
Hadoop Technical Lead (Assistant Consultant) at a tech vendor with 10,001+ employees
* Distributed * Persistence * Offset management by consumer view full review »
Anonymous avatar x60
Senior Java Consultant at a tech services company with 501-1,000 employees
The most valuable features are performance, persistent messaging, and reliability. It allows us to persist the message for a configurable number of days, even after it has been delivered to the consumer. The message delivery is also fast. view full review »
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Senior Software Engineering Consultant at a tech services company with 51-200 employees
Kafka, as compared with other messaging system options, is great for large scale message processing applications. It offers high throughput with built-in fault-tolerance and replication. Messaging systems in general allow for logical and temporal decoupling between applications. Given Kafka's high availability, it's a great option to use if applications require availability, but not real-time processing. If a downstream system is offline, messages can queue up and process when possible, but the user may not necessarily need to be aware of any issues. A messaging-based architecture becomes important as a set of micro-services need to scale with high availability. Kafka is a great choice for messaging with such architecture. view full review »
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SDET II at a tech services company with 5,001-10,000 employees
* Replication, partitioning, and reliability are the most valuable features. * Even if one of my clusters fails, the replication factor of a topic makes sure that I have the data available for processing, so I won't lose any of it. * Partitioning enables me to process the parallel requests. It helps in reaching the throughput. view full review »
Anonymous avatar x60
Founder, CEO at a tech vendor with 1-10 employees
The ability to partition data on Kafka is valuable. But Kafka needs support and management. It is better to have it fully managed on the cloud. The only reason I give Kafka as product a low rating is because there are far superior and cheaper alternatives in cloud-based solutions, where we save money on manpower, electricity, servers, datacenters, networking, etc. In fact, this is the view I have for pretty much all open source software compared to cloud based services. They just make things cheaper, faster, scalable and manageable. Kafka is good, but Kafka as a cloud service is awesome!! This is a relative rating (compared to cloud services), not that something is wrong with Kafka. I hope that is clear. view full review »
B94acfff c187 47f3 a11a 2f35bba4f243 avatar
Solutions Architect at a consultancy with 1,001-5,000 employees
Apache Kafka is actually a distributed commit log. That is different than most messaging and queuing systems before it. I find the ability to write data at one velocity and have subscribing consumers read at different velocities to be the best feature. view full review »
D8cacc50 c74a 4062 b407 94a51a8ca547 avatar
Principal Software Architect at a tech services company with 11-50 employees
Real-time streaming and persistence into distributed nodes. It provides a simple mechanism to create, publish, and subscribe. view full review »
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Team Lead at a financial services firm with 1,001-5,000 employees
* Message Retention: Unlike regular message queues, messages stay in Kafka after clients consume them. A message can be consumed over and over again by the same or a different client until topic retention (by max data size or oldest message timestamp) kicks in and the oldest messages get deleted. This can be very handy in many scenarios: handling bugs in software, testing code, simple distribution of message processing, and routing messages to many different consumers simultaneously. * Horizontal Scalability: To add more capacity, both in terms of storage and performance to a Kafka cluster, you just need to add more servers. Regular message queues usually work in a master-slave configuration and do not scale very well horizontally. * Simplicity in operations. view full review »
Anonymous avatar x60
Head of Engineering
* Scalability * Reliability * Ease of use view full review »
50918253 dfac 4b79 a431 4fe711a85fff avatar
Deputy General Manager, DevOps Manager at a tech services company
One of the best features which I have worked with is replay. view full review »
A6947a15 28e5 4b37 adc2 c1655b92dd46 avatar
Java Architect at a tech vendor with 51-200 employees
Excellent speeds for publishing messages faster. view full review »
D94612f3 2860 4f98 8b91 b334119e0435 avatar
Big Data Lead at a marketing services firm with 51-200 employees
We are using Kafka consumer and producer. view full review »
Anonymous avatar x60
Enterprise Architect at a logistics company with 1,001-5,000 employees
* Supports more than 10,000 events/second. * Scalability * Replication It is a good product for event-driven architecture. view full review »
98daa8e2 c94b 4886 a904 8eece49fd0de avatar
Technical Lead/Project Manager(Consulting Apple Inc) at a tech services company with 1,001-5,000 employees
The most valuable features are topic-based eventing, scalability, and retention periods. view full review »
Anonymous avatar x60
Lead Engineer at a retailer with 1,001-5,000 employees
We use the product for high-scale distributed messaging. The processing capability of the product is enormous. Being a distributed platform, multiple consumers can sync with it and fetch messages. Another great feature is the consumer offset log which tells you where the consumer left and where he needs to start again. Consumers aren’t required to code and put extra effort to maintain the offset. view full review »
Anonymous avatar x60
Java Developer at a media company with 1,001-5,000 employees
The most valuable features to me are replication, partitioning and easy integration with Apache Spark, which we use quite a bit for distributed processing. Replication is good for high availability. It provides additional safety for data in case of node failure or data center outage. Partitioning is a really useful feature for parallelizing processing. We use Apache Spark to process data from a Kafka queue, and Spark is able to assign one executor to each Kafka partition. The more partitions we have, the more threads we can use to process data in parallel. This helps us achieve really good throughput. view full review »
868fac92 ebf8 4953 a45c b94dc0d0ab3f avatar
Technical Architect at a tech vendor with 51-200 employees
I like the performance and reliability of Kafka. I needed a data streaming buffer that could handle thousands of messages per second with at least one processing point for an analytics pipeline. Kafka fits this requirement very well, as it is a fast, distributed message broker. It definitely does exactly what it is designed to do. view full review »

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