Apache Kafka

Apache Kafka

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Score 9.1 out of 100
Apache Kafka

Overview

Recent Reviews

Kafka quick queue

8 out of 10
January 30, 2019
We are using Kafka as an ingress and egress queue for data being saved into a big data system. Kafka is also being used as a queue for …
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What is Apache Kafka?

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…

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Product Details

What is Apache Kafka?

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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Reviews and Ratings

 (101)

Ratings

Reviews

(1-13 of 13)
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Borislav Traykov | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • The pub/sub model
  • Quick data transfer - regardless of volume (if you have enough resources)
  • Ability to transfer large amounts of data consistently (non-binary)
  • 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.
Score 7 out of 10
Vetted Review
Verified User
Review Source
  • Undoubtedly, Kafka's high throughput and low latency feature are the highlights.
  • Kafka can scale horizontally very well.
  • The CLI and configuration details need to be worked out more in-depth. The naming convention of configuration is not so good and causing a lot of confusion. Sometimes there are too many configuration parameters to tune--requires the adopter to understand a lot of tricks like NFS entrapment, for example.
  • Lack of a good monitoring solution so far
Viral Patel | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • It handles large amount of data simultaneously. Makes application scalable.
  • It is able to handle real time data pipeline.
  • Resistant to node failure within the cluster.
  • Does not have complete set of monitoring tools.
  • It does not support wild card topic selection.
  • Brokers and consumer pattern reduces the performance.
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • 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.
  • Apache Kafka does take some initial setup and deployment time especially if you haven't bought support from Confluent.
  • It is not a full solution so for an analytics use case, you will still need something like Tibco.
  • It does not have a SQL based query engine out-of-the-box so building/using analytics on top can be a lot of work. It would be great to have something already baked into Kafka out-of-the-box.
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • 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).
  • Doesn't work well with many small topics (on the order of thousands). There is a physical limit due to file handler usage on the number of topics Kafka can have before it grinds to a halt. This is not an issue for most people but it became an issue for us, as we need to have many, many topics and so we weren't able to fully migrate to Kafka except for a few of our big queues.
  • Lack of tenant isolation: if a partition on one node starts to lag on consume or publish, then all the partitions on that node will start to lag. That's what we've noticed and it's really frustrating to our customers that another customer's bad data affects them as well.
  • I don't have tooo much experience here, but I hear from other engineers on my team that the CLI admin tool is a real pain to use. For example, they say the arguments have no clear naming convention so they are hard to memorize and sometime you have to pass in undocumented properties.
January 30, 2019

Kafka quick queue

Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Fast queuing
  • Easy to set up and configure
  • Easy to add and remove queues
  • User interface for configuration could be a little better
  • Could be a little more defined when configuring files
  • Logging is a little hard to follow
Juan Francisco Tavira | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • High volume/performance throughput environments
  • Low latency projects
  • Multiple consumers for the same data, reprocessing, long-lasting information
  • Still a bit inmature, some clients have required recoding in the last few versions
  • New feaures coming very fast, several upgrades a year may be required
  • Not many commercial companies provide support