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."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."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"Allows us to process batch data, stream to real-time and build pipelines."
"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."
"Apache Flink's best feature is its data streaming tool."
"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."
"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."
"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 integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"The most valuable features are the IoT hub and the Blob storage."
"It's scalable as a cloud product."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The life cycle, report management and crash management features are great."
"We find the query editor feature of this solution extremely valuable for our business."
"It's a product that can scale."
"The solution's most valuable feature is its ability to create a query using SQ."
"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 should improve its data capability and data migration."
"The solution could be more user-friendly."
"There is room for improvement in the initial setup process."
"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."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"There is a learning curve. It takes time to learn."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"Its features for event imports and architecture could be enhanced."
"The solution's interface could be simpler to understand for non-technical people."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"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."
"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."
"The initial setup is complex."
"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."
"I would like to have a contact individual at Microsoft."
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 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.