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Apache NiFi vs Apache Spark comparison

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7,615 views|4,260 comparisons
Apache Spark Logo
9,844 views|7,892 comparisons
Featured Review
Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: January 2022.
566,121 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:
Pros
"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."

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"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.""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 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."

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Cons
"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."

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"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."

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Pricing and Cost Advice
  • "It's an open-source solution."
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  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
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    Questions from the Community
    Top Answer: 
    I am not involved in this area. I don't have any visibility in the costing and licensing. We have a department that goes through the licensing and once we approve the functions and the performance of… more »
    Top Answer: 
    I use Apache NiFi to build workflows. It's an event that is used for distributed messaging. You need to transfer the message that comes into Kafka Broker Topic. You get the messages in the Kafka queue… more »
    Top Answer: 
    I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer: 
    The solution has been very stable.
    Top Answer: 
    We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
    Ranking
    4th
    out of 14 in Compute Service
    Views
    7,615
    Comparisons
    4,260
    Reviews
    3
    Average Words per Review
    518
    Rating
    7.7
    2nd
    out of 14 in Compute Service
    Views
    9,844
    Comparisons
    7,892
    Reviews
    8
    Average Words per Review
    457
    Rating
    8.8
    Comparisons
    Learn More
    Overview
    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

    Offer
    Learn more about Apache NiFi
    Learn more about Apache Spark
    Sample Customers
    Macquarie Telecom Group, Dovestech, Slovak Telekom, Looker, Hastings Group
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company25%
    Comms Service Provider19%
    Financial Services Firm10%
    Manufacturing Company6%
    REVIEWERS
    Financial Services Firm40%
    Computer Software Company20%
    Marketing Services Firm10%
    Non Profit10%
    VISITORS READING REVIEWS
    Computer Software Company23%
    Comms Service Provider21%
    Financial Services Firm11%
    Media Company8%
    Company Size
    No Data Available
    REVIEWERS
    Small Business40%
    Midsize Enterprise20%
    Large Enterprise40%
    Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: January 2022.
    566,121 professionals have used our research since 2012.

    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.

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