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.
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"The scalability has been the most valuable aspect of the solution."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The data processing framework is good."
"The fault tolerant feature is provided."
"We use Spark to process data from different data sources."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"It's a product that can scale."
"We use Azure Stream Analytics for simulation and internal activities."
"Provides deep integration with other Azure resources."
"The most valuable features are the IoT hub and the Blob storage."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"The solution's most valuable feature is its ability to create a query using SQ."
"The life cycle, report management and crash management features are great."
"The way it organizes data into tables and dashboards is very helpful."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The migration of data between different versions could be improved."
"The product could improve the user interface and make it easier for new users."
"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."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"The collection and analysis of historical data could be better."
"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."
"If something goes wrong, it's very hard to investigate what caused it and why."
"Easier scalability and more detailed job monitoring features would be helpful."
"The UI should be a little bit better from a usability perspective."
"The solution's interface could be simpler to understand for non-technical people."
"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."
Apache Spark is ranked 1st in Hadoop with 60 reviews while Azure Stream Analytics is ranked 3rd in Streaming Analytics with 22 reviews. Apache Spark is rated 8.4, while Azure Stream Analytics is rated 8.2. 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.
