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."As an open-source solution, using it is basically free."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"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 platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is very stable and reliable."
"Technical support is pretty helpful."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"It's scalable as a cloud product."
"The solution has a lot of functionality that can be pushed out to companies."
"The solution's most valuable feature is its ability to create a query using SQ."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop."
"The most valuable features are the IoT hub and the Blob storage."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"Integrating event-level streaming capabilities could be beneficial."
"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 itself could be easier to use."
"We would like to have the ability to do arbitrary stateful functions in Python."
"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."
"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."
"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 solution’s customer support could be improved."
"The collection and analysis of historical data could be better."
"Early in the process, we had some issues with stability."
"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."
"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.