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 better than average and some of the valuable features include efficiency and stability."
"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."
"As an open-source solution, using it is basically free."
"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 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."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"It's scalable as a cloud product."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"It provides the capability to streamline multiple output components."
"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."
"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."
"We use Azure Stream Analytics for simulation and internal activities."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"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."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"Integrating event-level streaming capabilities could be beneficial."
"We would like to have the ability to do arbitrary stateful functions in Python."
"It was resource-intensive, even for small-scale applications."
"The initial setup is quite complex."
"In terms of improvement, the UI could be better."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"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."
"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."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"If something goes wrong, it's very hard to investigate what caused it and why."
"The collection and analysis of historical data could be better."
"The solution offers a free trial, however, it is too short."
"The solution’s customer support could be improved."
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.