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."
"The most valuable feature has been the range of clients and the range of connectors that we could use."
"The most valuable features of this solution are ease of use and implementation."
"The user interface is good and makes it easy to design very popular workflows."
"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."
"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."
"We can integrate the tool with other applications easily."
"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."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"ETL and streaming capabilities."
"Spark can handle small to huge data and is suitable for any size of company."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"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 solution has been very stable."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The use case templates could be more precise to typical business needs."
"More features must be added to the product."
"There are some claims that NiFi is cloud-native but we have tested it, and it's not."
"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."
"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."
"There should be a better way to integrate a development environment with local tools."
"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 initial setup was not easy."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"One limitation is that not all machine learning libraries and models support it."
"The migration of data between different versions could be improved."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"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."
"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 want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
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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