Apache NiFi vs Apache Spark comparison

Apache NiFi and Apache Spark are both solutions in the Compute Service category. Apache NiFi is ranked #8 with an average rating of 7.6, while Apache Spark is ranked #4 with an average rating of 8.7. Apache NiFi holds a 7.9% mindshare in CS, compared to Apache Spark’s 11.1% mindshare. Additionally, 90% of Apache NiFi users are willing to recommend the solution, compared to 90% of Apache Spark users who would recommend it.

Apache NiFi

Read 11 Apache NiFi reviews

2,418 Views
1,132 Comparison Views

90% willing to recommend

Apache Spark

Read 64 Apache Spark reviews

2,000 Views
1,515 Comparison Views

90% willing to recommend

Apache NiFi

Apache Spark

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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: December 2024).

Buyer's Guide

Apache NiFi vs. Apache Spark

December 2024

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Categories and Ranking

Apache NiFi

Ranking in Compute Service

8th

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark

Ranking in Compute Service

4th

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Java Frameworks (2nd)

Mindshare comparison

As of December 2024, in the Compute Service category, the mindshare of Apache NiFi is 7.9%, up from 6.0% compared to the previous year. The mindshare of Apache Spark is 11.1%, up from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Bruno_Silva

Partner at Timestamp, SA

Very easy to schedule jobs that realize improvements and monetize

The use case templates could be more precise to typical business needs. Available templates and model workflows are very high-level so don't really match real needs. It would help to have templates that allow us to see business opportunities. It would help to be able to copy workflow to another device rather than having to ingest it.

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SurjitChoudhury

Data engineer at Cocos pt

Offers batch processing of data and in-memory processing in Spark greatly enhances performance

Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.

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Quotes from Members

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

Pros

"The most valuable features of this solution are ease of use and implementation."

"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."

"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."

"The most valuable feature has been the range of clients and the range of connectors that we could use."

"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."

"Visually, this is a good product."

"The initial setup is very easy."

"The user interface is good and makes it easy to design very popular workflows."

More Apache NiFi pros

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

"I feel the streaming is its best feature."

"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."

"Provides a lot of good documentation compared to other solutions."

"We use Spark to process data from different data sources."

"ETL and streaming capabilities."

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

More Apache Spark pros

Cons

"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 should be a better way to integrate a development environment with local tools."

"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 use case templates could be more precise to typical business needs."

"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."

"More features must be added to the product."

"The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."

"There are some claims that NiFi is cloud-native but we have tested it, and it's not."

More Apache NiFi cons

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"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."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"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."

More Apache Spark cons

Pricing and Cost Advice

"We use the free version of Apache NiFi."

"It's an open-source solution."

"The solution is open-source."

"I used the tool's free version."

"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."

"It is an open-source platform. We do not pay for its subscription."

"Spark is an open-source solution, so there are no licensing costs."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"Apache Spark is an open-source tool."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

15%

Manufacturing Company

Retailer

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache NiFi?

The initial setup is very easy.

See all answers

What is your experience regarding pricing and costs for Apache NiFi?

I used the tool's free version.

See all answers

What needs improvement with Apache NiFi?

The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases.

See all answers

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

See all answers

Comparisons

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Compared 30% of the time

AWS Lambda vs Apache NiFi

Compared 25% of the time

Azure Stream Analytics vs Apache NiFi

Compared 8% of the time

AWS Fargate vs Apache NiFi

Compared 8% of the time

Apache Storm vs Apache NiFi

Compared 6% of the time

More Apache NiFi Competitors

Spring Boot vs Apache Spark

Compared 29% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

AWS Fargate vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Apache NiFi

December 2024

Download Apache NiFi product report

Buyer's Guide

Apache Spark

December 2024

Download Apache Spark product report

Learn More

Apache

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

Buyer's Guide

Apache NiFi vs. Apache Spark

December 2024

Free Report: Apache NiFi vs. Apache Spark

Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: December 2024.

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