We performed a comparison between Apache Flink and PubSub+ Event Broker based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."
"The setup was not too difficult."
"This is truly a real-time solution."
"The documentation is very good."
"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."
"Easy to deploy and manage."
"In my assessment of Solace against other products — as I was responsible for evaluating various products and bringing the right tool into companies in the past — I worked with multiple platforms like RabbitMQ, Confluent, Kafka, and various other tools in the market. But I found the event mesh capability to be a very interesting as well as fulfilling capability, towards what we want to achieve from a digital-integration-strategy point of view... It's distributed, yet it is intelligently connected. It can also span and I can plug and play any number of brokers into the event mesh, so it's a great deal. That's a differentiator."
"When it comes to granularity, you can literally do anything regarding how the filtering works."
"As of now, the most valuable aspects are the topic-based subscription and the fanout exchange that we are using."
"When we went to add another installation in our private cloud, it was easy. We received support from Solace and the install was seamless with no issues."
"One of the main reasons for using PubSub+ is that it is a proper event manager that can handle events in a reactive way."
"The way we can replicate information and send it to several subscribers is most valuable. It can be used for any kind of business where you've got multiple users who need information. Any company, such as LinkedIn, with a huge number of subscribers and any business, such as publishing, supermarket, airline, or shipping can use it."
"The most valuable feature of PubSub+ Event Broker is the scaling integration. Prior to using the solution, it was done manually with a file, and it can be done instantly live."
"The topic hierarchy is pretty flexible. Once you have the subject defined just about anybody who knows Java can come onboard. The APIs are all there."
"Apache Flink should improve its data capability and data migration."
"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."
"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."
"In a future release, they could improve on making the error descriptions more clear."
"There is a learning curve. It takes time to learn."
"We've pointed out some things with the DMR piece, the event mesh, in edge cases where we could see a problem. Something like 99 percent of users wouldn't ever see this problem, but it has to do with if you get multiple bad clients sending data over a WAN, for example. That could then impact other clients."
"The product should allow third-party agents to be installed. Currently, it is quite proprietary."
"The ease of management could be approved. The GUI is very good, but to configure and manage these devices programmatically in the software version is not easy. For example, if I would like to spin up a new software broker, then I could in theory use the API, but it would require a considerable amount of development effort to do so. There should be a tool, or something that Solace supports, that we could use for this, e.g., a platform like Terraform where we could use infrastructure as code to configure our source appliances."
"Some of the feature's gaps with some of the open-source vendors have been closed in a lot of ways. Being more agile and addressing those earlier could be an area for improvement."
"The integrations could improve in PubSub+ Event Broker."
"One of the areas of improvement would be if we could tell the story a bit better about what an event mesh does or why an event mesh is foundational to a large enterprise that has a wide diversity of applications that are homegrown and a small number off the shelf."
"The deployment process is complex."
"The licensing and the cost are the major pitfalls."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while PubSub+ Event Broker is ranked 10th in Streaming Analytics with 15 reviews. Apache Flink is rated 7.6, while PubSub+ Event Broker is rated 8.6. The top reviewer of Apache Flink writes "A great solution with an intricate system and allows for batch data processing". On the other hand, the top reviewer of PubSub+ Event Broker writes "Event life cycle management changes the way a designer or architect will design a topic and discover what is available". Apache Flink is most compared with Amazon Kinesis, Spring Cloud Data Flow, Databricks, Azure Stream Analytics and SAP Event Stream Processor, whereas PubSub+ Event Broker is most compared with Apache Kafka, IBM MQ, ActiveMQ, VMware RabbitMQ and Confluent. See our Apache Flink vs. PubSub+ Event Broker report.
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