Apache NiFi vs Apache Spark comparison

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Executive Summary

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.
To learn more, read our detailed Apache NiFi vs. Apache Spark Report (Updated: March 2024).
765,234 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"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."

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"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."

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Cons
"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."

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"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."

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Pricing and Cost Advice
  • "It's an open-source solution."
  • "We use the free version of Apache NiFi."
  • "The solution is open-source."
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  • "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."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "We are using the free version of the solution."
  • "Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
  • "Apache Spark is an expensive solution."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
  • "It is an open-source solution, it is free of charge."
  • More Apache Spark Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:I am using it open source, so it means it's free for me to use.
    Top Answer: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… more »
    Top Answer:The product’s most valuable features are lazy evaluation and workload distribution.
    Top Answer:They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.
    Top Answer:They could improve the issues related to programming language for the platform.
    Ranking
    8th
    out of 16 in Compute Service
    Views
    3,937
    Comparisons
    1,969
    Reviews
    5
    Average Words per Review
    565
    Rating
    7.4
    5th
    out of 16 in Compute Service
    Views
    3,209
    Comparisons
    2,461
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    Comparisons
    Spring Boot logo
    Compared 32% of the time.
    AWS Batch logo
    Compared 10% of the time.
    Spark SQL logo
    Compared 10% of the time.
    SAP HANA logo
    Compared 7% of the time.
    Amazon EMR logo
    Compared 4% of the time.
    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

    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
    Financial Services Firm18%
    Computer Software Company16%
    Government7%
    Manufacturing Company7%
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    Company Size
    REVIEWERS
    Small Business40%
    Large Enterprise60%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise11%
    Large Enterprise70%
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    Buyer's Guide
    Apache NiFi vs. Apache Spark
    March 2024
    Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    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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    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.