We performed a comparison between Apache NiFi and Apache Spark based on real PeerSpot user reviews.
Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The initial setup is very easy."
"The most valuable features of this solution are ease of use and implementation."
"The most valuable feature has been the range of clients and the range of connectors that we could use."
"The user interface is good and makes it easy to design very popular workflows."
"Visually, this is a good product."
"We can integrate the tool with other applications easily."
"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."
"The initial setup is very easy. I would rate my experience with the initial setup a ten out of ten, where one point is difficult, and ten points are easy."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"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."
"The processing time is very much improved over the data warehouse solution that we were using."
"There should be a better way to integrate a development environment with local tools."
"I think the UI interface needs to be more user-friendly."
"The use case templates could be more precise to typical business needs."
"We run many jobs, and there are already large tables. When we do not control NiFi on time, all reports fail for the day. So it's pretty slow to control, and it has to be improved."
"The overall stability of this solution could be improved. In a future release, we would like to have access to more features that could be used in a parallel way. This would provide more freedom with processing."
"There is room for improvement in integration with SSO. For example, NiFi does not have any integration with SSO. And if I want to give some kind of rollback access control across the organization. That is not possible."
"There are some claims that NiFi is cloud-native but we have tested it, and it's not."
"More features must be added to the product."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"The initial setup was not easy."
"The migration of data between different versions could be improved."
"The product could improve the user interface and make it easier for new users."
"There were some problems related to the product's compatibility with a few Python libraries."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
Apache NiFi is ranked 8th in Compute Service with 11 reviews while Apache Spark is ranked 5th in Compute Service with 60 reviews. Apache NiFi is rated 7.8, while Apache Spark is rated 8.4. The top reviewer of Apache NiFi writes "Allows the creation and use of custom functions to achieve desired functionality but limitation in handling monthly transactions due to a lack of partitioning for dates". On the other hand, the top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". Apache NiFi is most compared with Google Cloud Dataflow, AWS Lambda, Azure Stream Analytics, Apache Storm and AWS Fargate, whereas Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Amazon EMR. See our Apache NiFi vs. Apache Spark report.
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