We performed a comparison between Apache Spark Streaming and Google Cloud 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."
"The solution is very stable and reliable."
"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."
"The solution is better than average and some of the valuable features include efficiency and stability."
"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."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"The solution allows us to program in any language we desire."
"The service is relatively cheap compared to other batch-processing engines."
"The support team is good and it's easy to use."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"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."
"The initial setup is quite complex."
"In terms of improvement, the UI could be better."
"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."
"The solution itself could be easier to use."
"It was resource-intensive, even for small-scale applications."
"Google Cloud Dataflow should include a little cost optimization."
"The solution's setup process could be more accessible."
"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
"The deployment time could also be reduced."
"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."
"They should do a market survey and then make improvements."
"The authentication part of the product is an area of concern where improvements are required."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 8 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Apache Spark Streaming is rated 8.0, while Google Cloud Dataflow is rated 7.8. 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 Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Apache Spark Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Azure Stream Analytics, Confluent and Apache Pulsar, whereas Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Amazon Kinesis and Spring Cloud Data Flow. See our Apache Spark Streaming vs. Google Cloud Dataflow report.
See our list of best Streaming Analytics vendors.
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.
