We performed a comparison between Apache Flink and Azure Stream Analytics 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."It is user-friendly and the reporting is good."
"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."
"The setup was not too difficult."
"The documentation is very good."
"Apache Flink's best feature is its data streaming tool."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"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 product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."
"The solution's technical support is good."
"Technical support is pretty helpful."
"We use Azure Stream Analytics for simulation and internal activities."
"The solution's most valuable feature is its ability to create a query using SQ."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The most valuable features are the IoT hub and the Blob storage."
"It's scalable as a cloud product."
"I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop."
"Apache Flink's documentation should be available in more languages."
"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."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"There is a learning curve. It takes time to learn."
"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."
"In a future release, they could improve on making the error descriptions more clear."
"The solution could be more user-friendly."
"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."
"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."
"The solution offers a free trial, however, it is too short."
"If something goes wrong, it's very hard to investigate what caused it and why."
"The solution's interface could be simpler to understand for non-technical people."
"Its features for event imports and architecture could be enhanced."
"The collection and analysis of historical data could be better."
"Early in the process, we had some issues with stability."
"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Azure Stream Analytics is ranked 4th in Streaming Analytics with 22 reviews. Apache Flink is rated 7.6, while Azure Stream Analytics is rated 8.2. 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 Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". Apache Flink is most compared with Amazon Kinesis, Spring Cloud Data Flow, Databricks, Apache Pulsar and Google Cloud Dataflow, whereas Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Amazon MSK, Apache Spark and Apache Spark Streaming. See our Apache Flink vs. Azure Stream Analytics report.
See our list of best Streaming Analytics vendors.
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