Apache Kafka Reviews

I primarily use Kafka in the investment banking sector to update prices and inform clients of updates.

Kafka's most valuable feature is its user-friendliness.

There are some latency problems with Kafka.

I've been using Kafka for more than three years.

I would give Kafka a rating of seven out of ten.

My primary use case for Apache Kafka is replacing ETL and doing data transformations.

Kafka allows you to handle huge amounts of data and classify it into different categories. If you have huge amounts of data, Kafka is a very good solution for data classification. When you need to route it in different directions, you have to take a look at the messages that you get, interfile them, and then send them to the correct place. Kafka is a good product to use in the backend.

The feature I find most valuable is the classification feature. Kafka enables you to tag content with a category.

Kafka contains two components. The component that does the synchronization between the rest of the components, that's an older version of the software and it causes all kinds of configuration problems. The Confluent, which is the company that sells a commercial version of Kafka is getting away from that component precisely because of that. Kafka is a nightmare to administer.

In the next release, I would like to see that one troublesome component that causes configuration issues removed.

I have been using Apache Kafka for a couple of years.

The stability of this solution depends on whether it is properly configured. Having said that, Kafka is incredibly complex to configure, set up, administer, and maintain.

My opinion is that Apache Kafka is a scalable solution. In our organization, there are hundreds of thousands of users using Kafka.

The initial setup was extremely complex. In our case, it took a team of 12 two months to deploy.

These systems were installed by somebody else, not me.

I would advise others to schedule a month or two to just set it up and have it up and running.

There are other options. For example, Databricks is a Kafka alternative. We decided to go with Kafka because one of our clients already chose Kafka.

While evaluating, we found out Databricks is more expensive, for the level of activity that Kafka handles (in this case, millions of requests per day). Databricks could do it, but it would be overly expensive.

I would rate Apache Kafka's pricing a seven out of ten, with one being cheap and 10 being very expensive.

Since it has become so popular, large enterprises especially want to do it. For smaller enterprises, Kafka would probably be too expensive because they would have to hire people to maintain it.

I would rate the Apache Kafka solution a seven out of ten.

We use Apache Kafka primarily to queue the transactions or total the transactions.

Apache Kafka has helped our organization handle larger volumes without affecting the infrastructure load.

All the features of Apache Kafka are valuable, I cannot single out one feature.

Prioritization of messages in Apache Kafka could improve.

I have been using Apache Kafka for approximately six years.

The stability of Apache Kafka is very good.

Apache Kafka is the most scalable solution in the market.

I have not used the support from Apache Kafka.

Apache Kafka is straightforward to implement.

We did the implementation of Apache Kafka in-house.

I did not evaluate other solutions.

I rate Apache Kafka a nine out of ten.

We have a camera monitoring security system, in which we post messages onto the queue, which involves various steps in processing the message, like checking for the number of clients, running it against the police data, etc. So Apache Kafka is a security application with many types of consumers. We set up a workflow system with different sites, which works well.

As a software developer, I have found Apache Kafka's support to be the most valuable. The support team sends available information regarding the library and how to use the plugins. The solution is easy to integrate with any of our systems. We have other alternatives, but this is the one that seems to be the most popular database support.

The solution should be easier to manage. It needs to improve its visualization feature in the next release.

I have been using it for three years.

It is a stable solution. We never faced any issues. I rate it a ten out of ten.

It is a scalable solution. We set up a category with different consumers balancing things, which works as I thought.

I did not contact the technical support as it was not required.

We used Linksys for visualization along with Confluence, but there needed to be more value. For us, Apache Kafka is the best solution based on the support and third-party systems as it builds our subsystems around because we have a lot of development teams.

The initial setup was straightforward because I've got a lot of experience in this field. But even for a junior person, it would be fine. There are so many resources, and it's very well documented as they are a premium service provider. So it makes the setup just easier.

The deployment takes a few days.

We set up a free cluster for this service because we use a lot of data. We use ZooKeeper to secure different products for instruction with the cluster. But, it was easy as it is a popular product, and much information is available. It can download data, like fifty gigs per day. We can effectively handle it all as well. I never developed any issues.

It's a premium product, so it is not price-effective for us.

Apache Kafka is an out-of-the-box, reliable solution. For people in the fiber business, we need a reliable solution, and this solution is hundred percent reliable. If it is set up correctly, it hardly has any issues due to the more extensive user base; even if there are issues, it is sorted by the community. I rate it nine out of ten.

We use Apache Kafka for financial purposes. Every time one of our subscribed customers is due for an insurance payment, Apache Kafka sends an automated notification to the customer to let them know that their bill is due.

Resiliency is great and also the fact that it handles different data formats. There is one data format that's universal across multiple application domains — Avro. It's pretty universal compared to JSON, XML, SQI, and other formats.

Some vendors don't offer extra features for monitoring. Some come with Linux for default monitoring. Monitoring is very important. If something is not working properly, then our subscribers won't receive a notification. You then have to trace it back to Kafka and find the glitch or the messaging sequence that hasn't been racked up correctly.

It should support Avro — which handles different data formats — as a default data format. It would be much more flexible if it did.

I have been using Apache Kafka for three years.

It seems to be quite stable.

Apache Kafka is Scalable. You can actually launch a server node or a broker. Three nodes and Zookeeper (the Kafka server management system) is optimal. If one of them goes down you can automatically launch another one. You can go three servers or brokers back — there's a repetition on each Kafka broker.

Apache Kafka is open-source. They don't offer technical support.

On a scale from one to ten, I would give Apache Kafka a rating of eight.

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.

We were using another commercial messaging engine, which was not scalable unless you paid more. Each hub that we provisioned was expensive. This solution is open source, which is much easier to use and doesn’t cost us anything.

This product guarantees at-least-once delivery. We have asked JIRA to provide features such as at-most-once delivery to remove duplicate message consumption.

We haven’t faced any issues so far. Some of the clusters churn millions of records per seconds with ease.

We have clustered environments and we haven’t seen any scalability issues. We can provision a new node in as little as 45 minutes.

It is open source, so support is in our own hands. The only option is to make a new feature request through JIRA. When multiple people in the community make a request for similar feature, it gets priority.

We switched from a previous solution mainly to reduce costs and to have a more scalable solution.

The initial setup was a bit complex in terms of how to manage it across data centers. But once it was setup, we never faced issues.

We evaluated multiple options, such as ActiveMQ and RabbitMQ. We leaned towards this solution.

I would advise others to start with non-SSL implementations and try to do PoCs. Afterwards, they should move towards more secure features.

The primary use case of the solution is for asset communication through our microservices.

The solution has allowed us to take the use cases provided by another communication tool and resolve those issues.

The most valuable feature is how persistent it is. For example, we are able to reprocess messages when we need to, we're able to recover methods to consume them.

The solution can be improved by reducing the cost to run it on the premises.

I have been using the solution for four years.

The stability of the solution is good.

The solution is scalable.

The initial setup was straightforward.

The implementation was through a vendor.

I have seen a return on investment with this solution.

I give the solution a nine out of ten.

We have 80 people using the solution and five people are required to maintain it.

I suggest using cloud services because the solution is expensive if you are using it on-premises.

We have got this product, which is meant for integration. So our use cases are essentially integrating with other systems, using any messaging stack. We use these products in Dev and QA and we have connectors for various different messaging applications. Apache Kafka just happens to be one of the messaging applications that we connect with. We also have our own messaging, it's called Enterprise Messaging Server and Rendezvous, we connect to those also. Our product is essentially used for integration. So we connect to almost all messaging applications.

The most valuable feature is the speed at which the solution can be deployed.

The solution can improve its cloud support.

I have been using the solution in Dev and QA for a few years.

The solution is stable.

The solution is scalable.

Since we are supporting various different messaging applications, we tend to use and support all the messaging applications that are popular. Like SQS, Google pops up, Active MQ, Rapid MQ, MQTT, and IBM MQ.

The open-source version is relatively straightforward to set up and only takes a few minutes.

We typically implement the solution in-house.

The solution is open source.

I give the solution an eight out of ten.

We test all the supported versions of the solution based on our customers' use.

We support our integration product. So we need to do dev and QA with Apache Kafka or any other messaging applications. But we do not provide support. The solution can be supported by someone else.

We don't need to have any specific staff for deployment. All the developers in QA can install and configure the solution. We don't have a separate person for maintenance.

Our team and our product dev and QAs all use the solution.

I think Apache Kafka is a good solution and I recommend it to others.