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."
"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."
"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."
"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."
"Visually, this is a good product."
"The most valuable features of this solution are ease of use and implementation."
"We use Spark to process data from different data sources."
"The most valuable feature of Apache Spark is its ease of use."
"The solution is scalable."
"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."
"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."
"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 can handle small to huge data and is suitable for any size of company."
"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 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."
"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."
"More features must be added to the product."
"It should support more programming languages."
"Apache Spark should add some resource management improvements to the algorithms."
"At the initial stage, the product provides no container logs to check the activity."
"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 setup I worked on was really complex."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"The solution’s integration with other platforms should be improved."
Apache NiFi is ranked 8th in Compute Service with 10 reviews while Apache Spark is ranked 5th in Compute Service with 58 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, AWS Fargate and Apache Storm, 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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