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 solution is scalable."
"The scalability has been the most valuable aspect of the solution."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"We use Spark to process data from different data sources."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"I feel the streaming is its best feature."
"We find the query editor feature of this solution extremely valuable for our business."
"Provides deep integration with other Azure resources."
"The way it organizes data into tables and dashboards is very helpful."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"Technical support is pretty helpful."
"The solution's most valuable feature is its ability to create a query using SQ."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"The most valuable features are the IoT hub and the Blob storage."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"It should support more programming languages."
"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."
"The initial setup was not easy."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"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 UI should be a little bit better from a usability perspective."
"The solution offers a free trial, however, it is too short."
"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."
"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's interface could be simpler to understand for non-technical people."
"Its features for event imports and architecture could be enhanced."
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 AWS Lambda, 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.