Compare Apache Flink vs. Azure Stream Analytics

Cancel
You must select at least 2 products to compare!
Most Helpful Review
Find out what your peers are saying about Apache Flink vs. Azure Stream Analytics and other solutions. Updated: January 2021.
455,962 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"The documentation is very good.""With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts.""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.""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.""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.""This is truly a real-time solution."

More Apache Flink Pros »

"Provides deep integration with other Azure resources.""The most valuable features are the IoT hub and the Blob storage.""Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."

More Azure Stream Analytics Pros »

Cons
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language.""The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing.""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 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 terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves.""The machine learning library is not very flexible."

More Apache Flink Cons »

"If something goes wrong, it's very hard to investigate what caused it and why.""There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting.""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."

More Azure Stream Analytics Cons »

Pricing and Cost Advice
"This is an open-source platform that can be used free of charge."

More Apache Flink Pricing and Cost Advice »

"The cost of this solution is less than competitors such as Amazon or Google Cloud."

More Azure Stream Analytics Pricing and Cost Advice »

report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
455,962 professionals have used our research since 2012.
Ranking
4th
out of 31 in Streaming Analytics
Views
4,715
Comparisons
4,139
Reviews
6
Average Words per Review
1,635
Rating
7.7
5th
out of 31 in Streaming Analytics
Views
5,506
Comparisons
4,831
Reviews
3
Average Words per Review
677
Rating
8.0
Popular Comparisons
Compared 26% of the time.
Compared 8% of the time.
Compared 4% of the time.
Compared 34% of the time.
Also Known As
FlinkASA
Learn
Apache
Microsoft
Overview

Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

AzureStream Analytics is a fully managed event-processing engine that lets you set up real-time analytic computations on streaming data.The data can come from devices, sensors, web sites, social media feeds, applications, infrastructure systems, and more.
Offer
Learn more about Apache Flink
Learn more about Azure Stream Analytics
Sample Customers
LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks
Top Industries
VISITORS READING REVIEWS
Computer Software Company32%
Comms Service Provider16%
Media Company14%
Financial Services Firm7%
VISITORS READING REVIEWS
Computer Software Company36%
Comms Service Provider20%
Retailer5%
Energy/Utilities Company5%
Find out what your peers are saying about Apache Flink vs. Azure Stream Analytics and other solutions. Updated: January 2021.
455,962 professionals have used our research since 2012.

Apache Flink is ranked 4th in Streaming Analytics with 6 reviews while Azure Stream Analytics is ranked 5th in Streaming Analytics with 3 reviews. Apache Flink is rated 7.6, while Azure Stream Analytics is rated 8.0. The top reviewer of Apache Flink writes "Scalable framework for stateful streaming aggregations". On the other hand, the top reviewer of Azure Stream Analytics writes "Effective Blob storage and the IoT hub save us a lot of time, and the support is helpful". Apache Flink is most compared with Amazon Kinesis, Google Cloud Dataflow, Spring Cloud Data Flow, Databricks and IBM Streams, whereas Azure Stream Analytics is most compared with Databricks, Apache Spark, Apache NiFi, Apache Spark Streaming and Google Cloud Dataflow. See our Apache Flink vs. Azure Stream Analytics report.

See our list of best Streaming Analytics vendors.

We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.