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 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'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 solution is better than average and some of the valuable features include efficiency and stability."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"As an open-source solution, using it is basically free."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is very stable and reliable."
"It's a product that can scale."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"It provides the capability to streamline multiple output components."
"The solution's technical support is good."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"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."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"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."
"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."
"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."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"I would like to have a contact individual at Microsoft."
"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."
"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."
"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."
"The initial setup is complex."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
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.
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