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."
"We can integrate the tool with other applications easily."
"Visually, this is a good product."
"The initial setup is very easy."
"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."
"The most valuable features of this solution are ease of use and implementation."
"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."
"The scalability has been the most valuable aspect of the solution."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"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."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"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."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The main feature that we find valuable is that it is very fast."
"There should be a better way to integrate a development environment with local tools."
"More features must be added to the product."
"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."
"I think the UI interface needs to be more user-friendly."
"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 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 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."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"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."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"Apache Spark provides very good performance The tuning phase is still tricky."
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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