We performed a comparison between Apache Flink and Confluent 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."
"This is truly a real-time solution."
"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."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"It is user-friendly and the reporting is good."
"Apache Flink's best feature is its data streaming tool."
"Allows us to process batch data, stream to real-time and build pipelines."
"The documentation is very good."
"I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and tools."
"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."
"A person with a good IT background and HTML will not have any trouble with Confluent."
"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."
"With Confluent Cloud we no longer need to handle the infrastructure and the plumbing, which is a concern for Confluent. The other advantage is that all portfolios have access to the data that is being shared."
"The most valuable feature that we are using is the data replication between the data centers allowing us to configure a disaster recovery or software. However, is it's not mandatory to use and because most of the features that we use are from Apache Kafka, such as end-to-end encryption. Internally, we can develop our own kind of product or service from Apache Kafka."
"The documentation process is fast with the tool."
"The solution can handle a high volume of data because it works and scales well."
"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."
"There is room for improvement in the initial setup process."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"Apache Flink should improve its data capability and data migration."
"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."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"The machine learning library is not very flexible."
"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."
"It would help if the knowledge based documents in the support portal could be available for public use as well."
"There is no local support team in Saudi Arabia."
"The Schema Registry service could be improved. I would like a bigger knowledge base of other use cases and more technical forums. It would be good to have more flexible monitoring features added to the next release as well."
"Confluence could improve the server version of the solution. However, most companies are going to the cloud."
"In Confluent, there could be a few more VPN options."
"The formatting aspect within the page can be improved and more powerful."
"There is a limitation when it comes to seamlessly importing Microsoft documents into Confluent pages, which can be inconvenient for users who frequently work with Microsoft Office tools and need to transition their content to Confluent."
"Currently, in the early stages, I see a gap on the security side. If you are using the SaaS version, we would like to get a fuller, more secure solution that can be adopted right out of the box. Confluence could do a better job sharing best practices or a reusable pattern that others have used, especially for companies that can not afford to hire professional services from Confluent."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Confluent is ranked 3rd in Streaming Analytics with 19 reviews. Apache Flink is rated 7.6, while Confluent is rated 8.4. 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 Confluent writes "Has good technical support services and a valuable feature for real-time data streaming ". Apache Flink is most compared with Amazon Kinesis, Spring Cloud Data Flow, Databricks, Azure Stream Analytics and Apache Spark Streaming, whereas Confluent is most compared with Amazon MSK, Amazon Kinesis, Databricks, AWS Glue and Oracle GoldenGate. See our Apache Flink vs. Confluent report.
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