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."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."
"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'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."
"The user interface is good and makes it easy to design very popular workflows."
"The most valuable feature has been the range of clients and the range of connectors that we could use."
"We can integrate the tool with other applications easily."
"The most valuable features of this solution are ease of use and implementation."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"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."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"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."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"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."
"It provides a scalable machine learning library."
"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."
"There are some claims that NiFi is cloud-native but we have tested it, and it's not."
"There should be a better way to integrate a development environment with local tools."
"The use case templates could be more precise to typical business needs."
"The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."
"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."
"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."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"The solution must improve its performance."
"The setup I worked on was really complex."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"There were some problems related to the product's compatibility with a few Python libraries."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Apache Spark provides very good performance The tuning phase is still tricky."
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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