We performed a comparison between Apache Flink and Apache Pulsar based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Databricks, Microsoft and others in Streaming Analytics."The documentation is very good."
"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."
"This is truly a real-time solution."
"Easy to deploy and manage."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"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."
"The setup was not too difficult."
"The solution operates as a classic message broker but also as a streaming platform."
"Apache Flink should improve its data capability and data migration."
"The machine learning library is not very flexible."
"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."
"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."
"In a future release, they could improve on making the error descriptions more clear."
"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."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"Documentation is poor because much of it is in Chinese with no English translation."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Apache Pulsar is ranked 12th in Streaming Analytics with 1 review. Apache Flink is rated 7.6, while Apache Pulsar is rated 8.0. 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 Apache Pulsar writes "The solution can mimic other APIs without changing a line of code". Apache Flink is most compared with Spring Cloud Data Flow, Amazon Kinesis, Databricks, Azure Stream Analytics and Google Cloud Dataflow, whereas Apache Pulsar is most compared with Apache Spark Streaming, Amazon Kinesis, Amazon MSK, Azure Stream Analytics and Google Cloud Dataflow.
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