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."
"The most valuable features of this solution are ease of use and implementation."
"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 initial setup is very easy."
"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."
"The product’s most valuable features are lazy evaluation and workload distribution."
"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."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"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."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"The solution is scalable."
"There should be a better way to integrate a development environment with local tools."
"More features must be added to the product."
"The use case templates could be more precise to typical business needs."
"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."
"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."
"It's not easy to install."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"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."
"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."
"Apache Spark's GUI and scalability could be improved."
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.
See our list of best Compute Service vendors.
We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
