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. 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 initial setup is very easy."
"The user interface is good and makes it easy to design very popular workflows."
"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 most valuable feature has been the range of clients and the range of connectors that we could use."
"Apache NiFi is user-friendly. Its most valuable features for handling large volumes of data include its multitude of integrated endpoints and clients and the ability to create cron jobs to run tasks at regular intervals."
"Visually, this is a good product."
"The solution has been very stable."
"Features include machine learning, real time streaming, and data processing."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"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 data processing framework is good."
"There's a lot of functionality."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"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."
"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."
"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 tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."
"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."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"There were some problems related to the product's compatibility with a few Python libraries."
"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."
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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