We performed a comparison between Apache Spark Streaming and Azure Stream Analytics 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."The solution is very stable and reliable."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"As an open-source solution, using it is basically free."
"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."
"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 better than average and some of the valuable features include efficiency and stability."
"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."
"It's scalable as a cloud product."
"The way it organizes data into tables and dashboards is very helpful."
"I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."
"The solution's technical support is good."
"The most valuable features are the IoT hub and the Blob storage."
"The solution has a lot of functionality that can be pushed out to companies."
"The life cycle, report management and crash management features are great."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"The solution itself could be easier 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."
"It was resource-intensive, even for small-scale applications."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"In terms of improvement, the UI could be better."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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."
"Early in the process, we had some issues with stability."
"The solution's interface could be simpler to understand for non-technical people."
"The solution’s customer support could be improved."
"The initial setup is complex."
"Its features for event imports and architecture could be enhanced."
"Easier scalability and more detailed job monitoring features would be helpful."
"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."
"The solution offers a free trial, however, it is too short."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 8 reviews while Azure Stream Analytics is ranked 3rd in Streaming Analytics with 22 reviews. Apache Spark Streaming is rated 8.0, while Azure Stream Analytics is rated 8.2. 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 Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". Apache Spark Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Apache Pulsar, Confluent and Starburst Enterprise, whereas Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Amazon MSK, Apache Flink and Confluent. See our Apache Spark Streaming vs. Azure Stream Analytics 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.