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."
"We can integrate the tool with other applications easily."
"The most valuable feature has been the range of clients and the range of connectors that we could use."
"Visually, this is a good product."
"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 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 product's deployment phase is easy."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The processing time is very much improved over the data warehouse solution that we were using."
"The solution has been very stable."
"The main feature that we find valuable is that it is very fast."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"Apache Spark can do large volume interactive data analysis."
"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."
"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 use case templates could be more precise to typical business needs."
"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."
"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."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"The initial setup was not easy."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"The solution must improve its performance."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
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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