Apache Kafka Reviews

69 Ratings
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Score 8.9 out of 100

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May 12, 2021
Anonymous | TrustRadius Reviewer
Score 8 out of 10
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Implemented Apache Kafka as a messaging layer at the enterprise level for an Insurance client. And it is the next level technology introduced to address the short comes of legacy JMS-based messaging layers like TIBCO EMS.
  • Scalable and flexibly.
  • User friendly simple configuration/setup.
  • Ability to process huge loads which is a much-needed key feature of a messaging layer. And I believe Kafka fulfills this criterion.
  • Adaptable for various integration products like Tibco, Mule, and even Java applications.
  • No queue/topic classification. I wouldn't say it is a problem, but we miss this feature we had in JMS based Messaging Service
  • Monitoring problems
Since the open-source version of Apache Kafka is available it will reduce the cost of paid messaging layer products like TIBCO EMS It's easy to find professional expertise. And before you start implementing Apache Kafka in any organization, you have the ability to create POCs, and by doing so, it gives us great comfort to embrace this technology.
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April 09, 2021
Anonymous | TrustRadius Reviewer
Score 7 out of 10
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Apache Kafka is used by our company as the "next generation" of messaging/data-streaming pipeline solutions, to replace our old legacy JMS-based messaging solution and enable the modern streaming API based applications. When it is used for messaging purposes, we shift the responsibility of data replay from the message source (publisher application) to the message destination (consumer application). This flexibility resolved the legacy issue of sources replaying the messages but impacting all subscribers to the same topic. When Kafka is used as the streaming pipeline, it is integrated seamlessly with the Spark/Spring Stream-based analytic solutions, as it is also a kind of distributed storage.
  • 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
When it is used as messaging, Apache Kafka is majorly preferred when the use case is Pub/Sub typed. It is not suitable to deal with the end-to-end queue use case nor the request/response paradigm. When Apache Kafka is used for streaming purposes, it doesn't have the native implementation of the query language, it is just a pipeline. You still need to put a lot of programming efforts into your streaming client-side to take care of those analytic requirements.
We are using the Apache open source version of Kafka. The community is a good place to ask questions. and we can get most of our problems resolved there.
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March 23, 2020
Viral Patel | TrustRadius Reviewer
Score 9 out of 10
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We used it for event logging. It was used for application log collection. Was used with exception tracking and with core microservices of the web application. It helped us reduce cost and simplified operational monitoring.
  • 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.
It works well as a replacement for traditional message broker. Used when you want to log simultaneously tracking multiple web activities.
They provide very good response. Sometimes they get queued up.
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November 06, 2019
Anonymous | TrustRadius Reviewer
Score 9 out of 10
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We use Kafka for two key features: (1) keeping a buffer of all the incoming records that need to be stored in our data infrastructure, and (2) having a way to replay messages in case our data infrastructure loses some data.
The reason we need to buffer is that when our traffic spikes, we can have up to 1 million messages coming in that need to be processed in some form or fashion. To expect the back-end service to support that is crazy. Instead, we dump them into Kafka to give our data infrastructure time to ingest them. As for replaying events, sometimes the ingestion pipeline fails and drops some messages. I know - that's a huge mistake on our engineering team's part - but when it does happen Kafka has the ability to rewind and replay messages, resulting in delayed processing but no data loss.
  • 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.
Despite the disadvantages I list, I really believe that Kafka is the right choice whenever you need a queueing or message broker system. Kafka is way too battle-tested and scales too well to ever not consider it. The only exception is if your use case requires many, many small topics. Also, Kafka doesn't support delay queues out of the box and so you will need to "hack" it through special code on the consumer side.
We use Heroku to host Pulsar and they have tons of Kafka experts that have helped us tune every little setting and give us advice via email or live chat (if you pay for premium support).
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November 07, 2019
Anonymous | TrustRadius Reviewer
Score 10 out of 10
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Apache Kafka is used as a stream/message ingestion engine for all the customer-facing apps including some internal streams company-wide. It is used to ingest close to 2-5 million small (few bytes) messages per second that are then used for internal analytics and decision making in realtime and feed analytics backend (Tibco Spotfire).
  • 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.
Apache Kafka is very well suited where the deployment entails getting a very large number of small messages at extremely high rates—4 million-plus messages a second. It is also very well suited when you need stronger ordering guarantees than a traditional messaging system can provide. It is less suited when you don't need such high message ingestion rates and need to do everything in a public cloud. Apache Kafka will be an overkill for such small/simple deployments.
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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March 01, 2018
Juan Francisco Tavira | TrustRadius Reviewer
Score 9 out of 10
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Apache Kafka is becoming the new standard for messaging at our organization. Originally we limited the use to big data environments and projects but as the technology is becoming more mature we think it will eventually replace classical messaging software.
  • 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
Apache Kafka is extremely well suited in near real-time scenarios, high volume or multi-location projects. It can solve escalation problems for a fraction of the cost other solutions do and it has the flexibility of open source scenarios.
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January 29, 2019
Anonymous | TrustRadius Reviewer
Score 8 out of 10
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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 frontend applications to use in order to retrieve data and analytics from MapR and HortonWorks.
  • 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
If you need a queue for ingest or user interfaces Kafka is a great tool. Easy on the admins as well as the developers.
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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 applications.
Categories:  Message Queue

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