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 very stable and reliable."
"It's the fastest solution on the market with low latency data on data transformations."
"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 has features like checkpointing and Streaming API that are useful."
"The solution is better than average and some of the valuable features include efficiency and stability."
"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."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"It provides the capability to streamline multiple output components."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"Provides deep integration with other Azure resources."
"The way it organizes data into tables and dashboards is very helpful."
"The solution's most valuable feature is its ability to create a query using SQ."
"It's a product that can scale."
"We find the query editor feature of this solution extremely valuable for our business."
"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."
"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 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."
"The solution itself could be easier to use."
"The initial setup is quite complex."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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."
"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."
"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 collection and analysis of historical data could be better."
"The UI should be a little bit better from a usability perspective."
"If something goes wrong, it's very hard to investigate what caused it and why."
"The solution's interface could be simpler to understand for non-technical people."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 8 reviews while Azure Stream Analytics is ranked 4th 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, 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.
See our list of best Streaming Analytics vendors.
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