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."Apache Flink's best feature is its data streaming tool."
"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."
"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."
"Easy to deploy and manage."
"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."
"It is user-friendly and the reporting is good."
"This is truly a real-time solution."
"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."
"The solution has a lot of functionality that can be pushed out to companies."
"The solution's technical support is good."
"The solution's most valuable feature is its ability to create a query using SQ."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"We find the query editor feature of this solution extremely valuable for our business."
"I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The machine learning library is not very flexible."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"There is room for improvement in the initial setup process."
"Apache Flink should improve its data capability and data migration."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"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."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"In a future release, they could improve on making the error descriptions more clear."
"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."
"Easier scalability and more detailed job monitoring features would be helpful."
"The solution's interface could be simpler to understand for non-technical people."
"The initial setup is complex."
"The solution’s customer support could be improved."
"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."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
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.
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.
