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."Easy to deploy and manage."
"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 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."
"Allows us to process batch data, stream to real-time and build pipelines."
"The documentation is very good."
"This is truly a real-time solution."
"Apache Flink's best feature is its data streaming tool."
"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."
"It's a product that can scale."
"We find the query editor feature of this solution extremely valuable for our business."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The way it organizes data into tables and dashboards is very helpful."
"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."
"The solution has a lot of functionality that can be pushed out to companies."
"Technical support is pretty helpful."
"The life cycle, report management and crash management features are great."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"There is room for improvement in the initial setup process."
"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."
"Apache Flink's documentation should be available in more languages."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"In a future release, they could improve on making the error descriptions more clear."
"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."
"Its features for event imports and architecture could be enhanced."
"The UI should be a little bit better from a usability perspective."
"The solution's interface could be simpler to understand for non-technical people."
"One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure."
"The initial setup is complex."
"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."
"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."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Azure Stream Analytics is ranked 3rd 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 Spring Cloud Data Flow, Amazon Kinesis, 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.
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