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 user interface is good and makes it easy to design very popular workflows."
"Visually, this is a good product."
"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."
"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 initial setup is very easy."
"We can integrate the tool with other applications easily."
"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."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"I feel the streaming is its best feature."
"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."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The product’s most valuable features are lazy evaluation and workload distribution."
"The solution is scalable."
"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."
"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."
"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."
"There should be a better way to integrate a development environment with local tools."
"More features must be added to the product."
"There are some claims that NiFi is cloud-native but we have tested it, and it's not."
"I think the UI interface needs to be more user-friendly."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The product could improve the user interface and make it easier for new users."
"The solution must improve its performance."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"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."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"At the initial stage, the product provides no container logs to check the activity."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
Apache NiFi is ranked 8th in Compute Service with 10 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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