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 fault tolerant feature is provided."
"This solution provides a clear and convenient syntax for our analytical tasks."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The data processing framework is good."
"The most valuable feature of Apache Spark is its ease of use."
"We use Spark to process data from different data sources."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The solution has been very stable."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"Technical support is pretty helpful."
"The way it organizes data into tables and dashboards is very helpful."
"Provides deep integration with other Azure resources."
"The solution's technical support is good."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"It provides the capability to streamline multiple output components."
"It's scalable as a cloud product."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"The logging for the observability platform could be better."
"The initial setup was not easy."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"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."
"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."
"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 collection and analysis of historical data could be better."
"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 offers a free trial, however, it is too short."
"The solution’s customer support could be improved."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"I would like to have a contact individual at Microsoft."
Apache Spark is ranked 1st in Hadoop with 60 reviews while Azure Stream Analytics is ranked 4th 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.
