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."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."
"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 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."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"The most valuable features are the IoT hub and the Blob storage."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The solution's most valuable feature is its ability to create a query using SQ."
"It's a product that can scale."
"Provides deep integration with other Azure resources."
"It was resource-intensive, even for small-scale applications."
"In terms of improvement, the UI could be better."
"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."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"Integrating event-level streaming capabilities could be beneficial."
"We would like to have the ability to do arbitrary stateful functions in Python."
"The initial setup is quite complex."
"The solution itself could be easier to use."
"Easier scalability and more detailed job monitoring features would be helpful."
"The collection and analysis of historical data could be better."
"The solution offers a free trial, however, it is too short."
"Early in the process, we had some issues with stability."
"If something goes wrong, it's very hard to investigate what caused it and why."
"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."
"The initial setup is complex."
"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 9 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.