"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 most valuable features of this solution are ease of use and implementation."
"The solution has been very stable."
"The processing time is very much improved over the data warehouse solution that we were using."
"Apache Spark can do large volume interactive data analysis."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The main feature that we find valuable is that it is very fast."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"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."
"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."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"It's not easy to install."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"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."
"The logging for the observability platform could be better."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Apache NiFi is ranked 4th in Compute Service with 3 reviews while Apache Spark is ranked 2nd in Compute Service with 9 reviews. Apache NiFi is rated 7.6, while Apache Spark is rated 8.4. The top reviewer of Apache NiFi writes "Open source solution that allows you to collect data with ease". On the other hand, the top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". Apache NiFi is most compared with Google Cloud Dataflow, AWS Lambda, Azure Stream Analytics, Apache Storm and IBM Streams, whereas Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Lambda, SAP HANA and Google Cloud Dataflow. 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.