We performed a comparison between Apache Spark Streaming and Cloudera DataFlow 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."It's the fastest solution on the market with low latency data on data transformations."
"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."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"The solution is better than average and some of the valuable features include efficiency and stability."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"The solution is very stable and reliable."
"As an open-source solution, using it is basically free."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"DataFlow's performance is okay."
"The initial setup was not so difficult"
"This solution is very scalable and robust."
"The most effective features are data management and analytics."
"Integrating event-level streaming capabilities could be beneficial."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"We would like to have the ability to do arbitrary stateful functions in Python."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"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."
"It was resource-intensive, even for small-scale applications."
"The initial setup is quite complex."
"In terms of improvement, the UI could be better."
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 9 reviews while Cloudera DataFlow is ranked 13th in Streaming Analytics with 4 reviews. Apache Spark Streaming is rated 8.0, while Cloudera DataFlow is rated 7.2. 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 Cloudera DataFlow writes "Has good data management and analytics features". Apache Spark Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Azure Stream Analytics, Apache Pulsar and Confluent, whereas Cloudera DataFlow is most compared with Databricks, Confluent, Amazon MSK, Hortonworks Data Platform and Informatica Data Engineering Streaming. See our Apache Spark Streaming vs. Cloudera DataFlow report.
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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.
