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."It's the fastest solution on the market with low latency data on data transformations."
"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."
"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."
"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."
"The solution is very stable and reliable."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The solution has a lot of functionality that can be pushed out to companies."
"Technical support is pretty helpful."
"The solution's most valuable feature is its ability to create a query using SQ."
"We use Azure Stream Analytics for simulation and internal activities."
"It's scalable as a cloud product."
"The life cycle, report management and crash management features are great."
"We find the query editor feature of this solution extremely valuable for our business."
"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."
"We would like to have the ability to do arbitrary stateful functions in Python."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"It was resource-intensive, even for small-scale applications."
"In terms of improvement, the UI could be better."
"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 initial setup is quite complex."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"The collection and analysis of historical data could be better."
"The solution's interface could be simpler to understand for non-technical people."
"Early in the process, we had some issues with stability."
"The solution’s customer support could be improved."
"The solution offers a free trial, however, it is too short."
"Its features for event imports and architecture could be enhanced."
"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."
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, Confluent, Apache Pulsar 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.