I use the solution for detection of streaming data.
Head of Data Science at a energy/utilities company with 10,001+ employees
Easy deployment and install, open-source, but underdeveloped API
Pros and Cons
- "The setup was not too difficult."
- "In a future release, they could improve on making the error descriptions more clear."
What is our primary use case?
What needs improvement?
I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development. Additionally, there are no managed services. For example, on Azure, you would have to set everything up yourself.
In a future release, they could improve on making the error descriptions more clear.
For how long have I used the solution?
I have been using the solution for two weeks.
Which solution did I use previously and why did I switch?
I have used many different competing solutions in the past such as Pyspark, Hadoop and Storm.
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How was the initial setup?
The setup was not too difficult.
What about the implementation team?
To deploy the solution locally it was relatively easy.
What's my experience with pricing, setup cost, and licensing?
The solution is open-source, which is free.
What other advice do I have?
When choosing this solution you have to look at your use case to see if this is the best choice for you. If you need to have super-fast realtime streaming, and you can develop in Scala, then it might make a lot of sense to use it. If you are looking at delays of seconds, and you are working on Python, then Pyspark might be a better solution.
I rate Apache Flink a six out of ten.
Disclosure: My company does not have a business relationship with this vendor other than being a customer.

CTO at ReNew
Helps us to create both simple and complex data processing tasks
Pros and Cons
- "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 should improve its data capability and data migration."
What is our primary use case?
We utilize IoT devices to gather data for our clients. This data is analyzed to produce reports and insights, and we leverage machine learning and artificial intelligence models.
What is most valuable?
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.
What needs improvement?
Apache Flink should improve its data capability and data migration.
For how long have I used the solution?
I have been using the product for five years.
What do I think about the stability of the solution?
The solution is stable.
What do I think about the scalability of the solution?
Apache Flink is scalable.
How was the initial setup?
The product's deployment can be completed in minutes, and we have a special team. It is straightforward. We initiate our requirements within our secure software and utilize Jenkins and our pipelines to carry out the deployment process, whether for expanding services on the cloud or on-premise servers.
What about the implementation team?
Apache Flink can be deployed in-house.
What other advice do I have?
I rate the product an eight out of ten.
Which deployment model are you using for this solution?
On-premises
Disclosure: My company does not have a business relationship with this vendor other than being a customer.

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Updated: August 2025
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