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Buyer's Guide
Message Queue (MQ) Software
June 2022
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Associate Principal Analyst at a computer software company with 10,001+ employees
Real User
Top 20
Helpful technical support and relatively easy to set up but is not cloud agnostic
Pros and Cons
  • "Technical support is pretty helpful."
  • "Early in the process, we had some issues with stability."

What is our primary use case?

We were doing some level of stream data processing, so we had some use cases which were related to IoT. We had some IoT devices getting data in from other IoT devices and Azure Streaming Analytics has a special streaming analytics offering for IoT devices. Basically it was used for that. 

What is most valuable?

I basically use two features that are useful. One is Azure Event Hubs, and that is used in conjunction with Azure Streaming Analytics. One is the broker and one is the processing engine. With the processing engine, the SQL way of dealing with things, with streams, is what I like, compared to other solutions, which are more like Scala or Spark-based, where you need to know the language. This was comparatively easy to use with its ability to write SQL on streams.

Technical support is pretty helpful. 

It's my understanding that the setup is pretty straightforward.

What needs improvement?

With Azure specifically, the drawback is it is a very Azure-specific product. You can't connect it to external things out of Azure. For example, Spark or Databricks can be used in any cloud and can be used in AWS. This product doesn't work that way and is very Azure-specific. It's not a hybrid solution and it's not a cloud-agnostic solution, where you put it on other clouds, et cetera. 

We had some connections which we wanted to make with AWS, which we couldn't do with this. We had to use something else for that.

Early in the process, we had some issues with stability.

You cannot do joins on streams of data. For example, one stream joining with another stream. Real-time to real-time joins, you're not able to do that. You can only join your stream with static data from your Azure storage. 

For how long have I used the solution?

I've used the solution for one and a half to two years.

What do I think about the stability of the solution?

There were some issues with the IoT jobs when streaming Azure Streaming Analytics, which are high proof now. That said, earlier, we used to have a lot of issues with the erratic behavior of jobs. If data is not in the way they expect it, if they are not modeled correctly, then the jobs tend to break or fail quite a lot. That was one issue we had.

How are customer service and technical support?

We've been in touch with technical support. There was a time when jobs failed a lot and we couldn't understand the reason. When we talked to the spec tech support, they've looked into our data and told us that it's not exactly modeled as how Azure Stream Analytics needs it. That wasn't very clear when we got it. 

They were helpful. There were issues which they handled, which they told us about. The communication was great.

We had the support package included.

Which solution did I use previously and why did I switch?

I'm now an analyst, so I don't use the products per se, however, prior to this, I have used Azure Streaming Analytics quite a lot. Currently, I'm working a bit on Databricks Spark Streaming. These two are, I would say, what I have used personally.

How was the initial setup?

The product was set up before I started out, however, what I can say, having set up some things personally, is it is comparatively straightforward and the Microsoft support on that is comparatively good.

What's my experience with pricing, setup cost, and licensing?

In terms of pricing, you can't compare it to open source solutions. It would be higher compared to open source, of course, however, with the support and everything you're getting, I would say the price, in general, is fair. 

I have seen AWS as well and can compare it to that and I would say it is fair. The problem is it is not exactly dynamic or serverless, with how the way things are in the cloud. Therefore, it is not completely utilized. You have to set up things beforehand with some level of capability and capacity beforehand. In regards to the price, it's not too high and also not too low.

Their pricing is not exactly serverless. It's per hour. A lot of others are moving towards pricing based on the amount of data you pull. Streaming Analytics charges per hour, and in that sense, you need to set up the capacity by trial and error, literally. 

Which other solutions did I evaluate?

I'm comparing the Azure Stream Analytics, AWS Kinesis, GCP Pub/Sub, and Dataflow. So I'm currently in the process of writing that research.

What other advice do I have?

If you are in the Azure world completely, and you're using the Microsoft stack completely, and you do not have the need to go in any other cloud, then it makes sense to use this solution as it integrates very well within the Azure ecosystem. 

For IoT use cases, if you want to do real-time dashboarding with Power BI, it's great. Those kinds of things are where it has its niche. However, if you want a cloud-agnostic kind of solution, where you do not want to be stuck with just Microsoft, then there are other solutions out there such as Confluent, Kafka, Spark Streaming with Databricks, et cetera. You'll get the flexibility you need using any of those platforms.

I'd rate the solution at a seven out of ten. We had some issues with the jobs not behaving properly. They promise a lot, however, sometimes that doesn't happen and we realized that later. Some things under the hood, we couldn't really understand and we needed to get in touch with support. Those kinds of issues are where I would say it needs a bit of improvement, and maybe that's why I cut off two or three points.

Which deployment model are you using for this solution?

Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Solution Architect at a manufacturing company with 10,001+ employees
Real User
Good performance when a high throughput is required, but they need to implement a portal
Pros and Cons
  • "The processing power of Apache Kafka is good when you have requirements for high throughput and a large number of consumers."
  • "They need to have a proper portal to do everything because, at this moment, Kafka is lagging in this regard."

What is our primary use case?

I am a solution architect and I used Apache Kafka in this role.

What is most valuable?

The processing power of Apache Kafka is good when you have requirements for high throughput and a large number of consumers. 

What needs improvement?

They need to have a proper portal to do everything because, at this moment, Kafka is lagging in this regard. It could be used to do the preprocessing or the configurations, instead of directly doing it on the queues or the topics. If you look at Solace, for example, they have come up with a portal where you don't need to touch these activities. You don't need to access the platform beyond the portal.

For how long have I used the solution?

I have used Apache Kafka for between one and one and a half years.

What do I think about the stability of the solution?

Apache Kafka is stable.

What do I think about the scalability of the solution?

This is certainly a scalable product. There are currently 30 or more people using it but we expect to scale beyond this. It is going to be an enterprise tool within the company.

How are customer service and technical support?

I am not directly interacting with the service people at this moment. It is limited for now because we are still exploring and effecting our architecture and design, and deciding how to align it with our existing strategy. There is not much progress in this regard and it will take more time.

Which solution did I use previously and why did I switch?

Prior to working with Apache Kafka, there was no messaging queue system. For many projects, they were using the Azure Event Hub, but it was not serving the purpose. So, we started moving towards Kafka, and that's why we have procured Confluent Kafka.

Several months ago, I stopped working on Apache Kafka. I am now working on Confluent Kafka. It was not my decision to switch solutions.

My current organization has chosen Confluent Kafka for various reasons. One is that we have a large number of streaming requirements, and Confluent Kafka has one more layer on top of Apache Kafka to do this transformation and connecting with other multiple lane systems.

There are out-of-the-box features along with the KSQL features. For example, things like fetching the events are kind of query-based. So, that seems to be a good feature for our requirements. That is why we ultimately procured Confluent Kafka.

For some time, I have also worked with Solace and it has an advantage. Given that my core strength is integration, I work with integration platforms such as MuleSoft, Azure functions, then TIBCO. Based on our requirements, I found that the event-driven APA implementation with Solace was easier.

Solace also has a top-notch solution for portal management and you register your producers, consumers, and preprocessing logic. All of these things are pretty easy to do. This is an area where Kafka could use some enhancement.

How was the initial setup?

I don't think that the initial setup was a complex process.

Which other solutions did I evaluate?

MQ messaging systems are not my core strength but for any integration platform where we have a large number of APIs and events, to integrate with an IoT platform, for example, I found Kafka is better than ActiveMQ.

I'm not getting into in MQTT or other things but comparatively, when you compare ActiveMQ and Kafka, Kafka has done better.

What other advice do I have?

I think that many people are using Apache Kafka just as a publishing and subscription model, but I feel that Kafka is better than that. Furthermore, Confluent Kafka is even more than that.

Confluent Kafka is offering features that are equal to those of a data lake. You can do lots with data, and huge data can be persisted. However, many people are not using that feature. Rather than make use of persistence logic, they are pushing the messages and consuming them. Maybe if people were using it for persistence, they would see the impact or real power of Kafka.

I would rate this solution a seven out of ten.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Buyer's Guide
Message Queue (MQ) Software
June 2022
Get our free report covering Apache, IBM, Apache, and other competitors of PubSub+ Event Broker. Updated: June 2022.
609,272 professionals have used our research since 2012.