Compare Apache NiFi vs. Apache Spark

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Apache NiFi Logo
7,694 views|4,889 comparisons
Apache Spark Logo
11,296 views|9,230 comparisons
Most Helpful Review
Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: January 2021.
456,249 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

"The most valuable features of this solution are ease of use and implementation.""Visually, this is a good product."

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"I found the solution stable. We haven't had any problems with it.""The scalability has been the most valuable aspect of the solution.""The most valuable feature of this solution is its capacity for processing large amounts of data.""The solution is very stable.""I feel the streaming is its best feature.""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.""The processing time is very much improved over the data warehouse solution that we were using."

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"There should be a better way to integrate a development environment with local tools.""There are some claims that NiFi is cloud-native but we have tested it, and it's not."

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"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.""The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.""When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.""The solution needs to optimize shuffling between workers.""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.""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.""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.""I would like to see integration with data science platforms to optimize the processing capability for these tasks."

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Pricing and Cost Advice
"It's an open-source solution."

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Questions from the Community
Top Answer: The most valuable features of this solution are ease of use and implementation.
Top Answer: There should be a better way to integrate a development environment with local tools. Most of the development is done on the console. For example, in Spark, we can develop on our local desktop and… more »
Top Answer: SQreamDB is a GPU DB. It is not suitable for real-time oltp of course. Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)… more »
Top Answer: I love every core functionality of Apache Spark Initially they have only provided RDD basic interface to process the data across distributed cluster. Then it evolved to dataframe and dataset interface… more »
Top Answer: Apache spark is available in cloud services like AWS cloud, Azure. We have to use the specific service for our use case. For example we can use AWS Glue which runs spark for ETL process, AWS EMR… more »
out of 13 in Compute Service
Average Words per Review
out of 13 in Compute Service
Average Words per Review
Popular Comparisons
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Compared 29% of the time.
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Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

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

Learn more about Apache NiFi
Learn more about Apache Spark
Sample Customers
Macquarie Telecom Group, Dovestech, Slovak Telekom, Looker, Hastings GroupNASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab,, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Top Industries
Computer Software Company28%
Comms Service Provider20%
Media Company10%
Financial Services Firm8%
Financial Services Firm44%
Computer Software Company22%
Marketing Services Firm11%
Non Profit11%
Computer Software Company26%
Comms Service Provider18%
Media Company11%
Financial Services Firm9%
Company Size
No Data Available
Small Business39%
Midsize Enterprise19%
Large Enterprise42%
Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: January 2021.
456,249 professionals have used our research since 2012.

Apache NiFi is ranked 3rd in Compute Service with 2 reviews while Apache Spark is ranked 1st in Compute Service with 12 reviews. Apache NiFi is rated 7.6, while Apache Spark is rated 8.2. 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 "Good Streaming features enable to enter data and analysis within Spark Stream". Apache NiFi is most compared with AWS Lambda, Google Cloud Dataflow, Azure Stream Analytics, Apache Storm and IBM Streams, whereas Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, SAP HANA and Cloudera Distribution for Hadoop. See our Apache NiFi vs. Apache Spark report.

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