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."
"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."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
"The way it organizes data into tables and dashboards is very helpful."
"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."
"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."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"The life cycle, report management and crash management features are great."
"It provides the capability to streamline multiple output components."
"In terms of improvement, the UI could be better."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"The initial setup is quite complex."
"It was resource-intensive, even for small-scale applications."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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 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 centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."
"The UI should be a little bit better from a usability perspective."
"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 solution could be improved by providing better graphics and including support for UI and UX testing."
"One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"If something goes wrong, it's very hard to investigate what caused it and why."
"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."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 6 reviews while Azure Stream Analytics is ranked 4th in Streaming Analytics with 12 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 deployment as a cluster and good documentation". On the other hand, the top reviewer of Azure Stream Analytics writes "Offers advanced features and flavors for data processing and analysis". 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.
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