We performed a comparison between Apache Spark Streaming 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."Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"It's the fastest solution on the market with low latency data on data transformations."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."
"As an open-source solution, using it is basically free."
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."
"The design of the product is extremely well built and it is highly configurable."
"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."
"The benefit is escaping email communication. Sometimes people ignore emails or put them into spam, but with Confluence, everyone sees the same text at the same time."
"A person with a good IT background and HTML will not have any trouble with Confluent."
"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."
"The most valuable feature of Confluent is the wide range of features provided. They're leading the market in this category."
"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."
"Integrating event-level streaming capabilities could be beneficial."
"It was resource-intensive, even for small-scale applications."
"We would like to have the ability to do arbitrary stateful functions in Python."
"The solution itself could be easier to use."
"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."
"The initial setup is quite complex."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"In terms of improvement, the UI could be better."
"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."
"Confluent's price needs improvement."
"It could be improved by including a feature that automatically creates a new topic and puts failed messages."
"In Confluent, there could be a few more VPN options."
"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."
"Areas for improvement include implementing multi-storage support to differentiate between database stores based on data age and optimizing storage costs."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 9 reviews while Confluent is ranked 4th in Streaming Analytics with 21 reviews. Apache Spark Streaming is rated 8.0, while Confluent is rated 8.4. The top reviewer of Apache Spark Streaming writes "Easy integration, beneficial auto-scaling, and good open-sourced support community". 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 Spark Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Azure Stream Analytics, Apache Pulsar and Starburst Enterprise, whereas Confluent is most compared with Amazon MSK, Amazon Kinesis, Databricks, AWS Glue and Oracle GoldenGate. See our Apache Spark Streaming vs. Confluent report.
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