We performed a comparison between Apache Spark vs.Azure Stream Analytics based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Apache Spark and Azure Stream Analytics come out about equal in this comparison. Some users are more satisfied with Apache Spark’s stability, and pricing, but Azure Stream Analytics has an edge when it comes to ROI and technical support.
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The main feature that we find valuable is that it is very fast."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"I feel the streaming is its best feature."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"We use Azure Stream Analytics for simulation and internal activities."
"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."
"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 solution's most valuable feature is its ability to create a query using SQ."
"The solution has a lot of functionality that can be pushed out to companies."
"We find the query editor feature of this solution extremely valuable for our business."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"Apache Spark's GUI and scalability could be improved."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Apache Spark should add some resource management improvements to the algorithms."
"The solution needs to optimize shuffling between workers."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The logging for the observability platform could be better."
"The solution’s customer support could be improved."
"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 doesn't handle large data packets very efficiently, which could be improved upon."
"Early in the process, we had some issues with stability."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"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 collection and analysis of historical data could be better."
Apache Spark is ranked 2nd in Hadoop with 58 reviews while Azure Stream Analytics is ranked 4th in Streaming Analytics with 21 reviews. Apache Spark is rated 8.4, while Azure Stream Analytics is rated 8.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". 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 is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Apache NiFi, whereas Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Amazon MSK, Apache Flink and Apache Spark Streaming.
We monitor all Hadoop 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.