We performed a comparison between Apache Spark Streaming and Starburst Enterprise based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Databricks, Microsoft and others in Streaming Analytics."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."
"The solution is very stable and reliable."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"It's the fastest solution on the market with low latency data on data transformations."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"As an open-source solution, using it is basically free."
"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."
"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."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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 cost and load-related optimizations are areas where the tool lacks and needs improvement."
"It was resource-intensive, even for small-scale applications."
"The initial setup is quite complex."
"The solution itself could be easier to use."
"Integrating event-level streaming capabilities could be beneficial."
"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."
Earn 20 points
Apache Spark Streaming is ranked 8th in Streaming Analytics with 9 reviews while Starburst Enterprise is ranked 19th in Streaming Analytics with 1 review. Apache Spark Streaming is rated 8.0, while Starburst Enterprise is rated 8.0. The top reviewer of Apache Spark Streaming writes "Easy integration, beneficial auto-scaling, and good open-sourced support community". On the other hand, Apache Spark Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Azure Stream Analytics, Apache Pulsar and Apache Flink, whereas Starburst Enterprise is most compared with Dremio, Starburst Galaxy, Alteryx, Databricks and Informatica Data Engineering Streaming.
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We monitor all Streaming Analytics 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.
