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Apache NiFi vs Apache Spark comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache NiFi
Ranking in Compute Service
8th
Average Rating
7.8
Number of Reviews
11
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
64
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
 

Mindshare comparison

As of November 2024, in the Compute Service category, the mindshare of Apache NiFi is 8.0%, up from 5.9% compared to the previous year. The mindshare of Apache Spark is 11.2%, up from 7.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service
 

Featured Reviews

SabinaZeynalova - PeerSpot reviewer
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
Apache NiFi is slow to control and needs to be improved. I have to run many jobs and there are already large tables, which can make it difficult to control NiFi on time. There is no one to tell me when there is an incident and my server is down. When we manually start the NiFi process, it is not always started correctly. We can write scripts to run when a message is received from Airflow saying that the firewall is not running. This script will automatically start all servers, including the application servers. It will also check the status of all my NiFi processes and send a callback message with the results. I have written down all the processes that are monitored. 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. In future releases, there are extra features I’d like to add. For example, NiFi is not suitable for migration, and the replication in NiFi is really not good. Because when you process ten years of data, you can't manage all the transactions; it is not enough. Moreover, the handling of monthly transactions is not enough due to a lack of partitioning for dates. And, when we grade a monthly ticket, we must process all data then rerun our ETL jobs. If it's possible, enhancing the partitioning in NiFi for features would be beneficial.
SurjitChoudhury - PeerSpot reviewer
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.

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."
"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 most valuable feature has been the range of clients and the range of connectors that we could use."
"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."
"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 of Apache Spark is its ease of use."
"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 most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"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."
"The main feature that we find valuable is that it is very fast."
"The data processing framework is good."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
 

Cons

"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."
"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 are some claims that NiFi is cloud-native but we have tested it, and it's not."
"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."
"I think the UI interface needs to be more user-friendly."
"The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."
"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."
"One limitation is that not all machine learning libraries and models support it."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"The main concern is the overhead of Java when distributed processing is not necessary."
"At the initial stage, the product provides no container logs to check the activity."
"The solution’s integration with other platforms should be improved."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"There were some problems related to the product's compatibility with a few Python libraries."
 

Pricing and Cost Advice

"It's an open-source solution."
"I used the tool's free version."
"The solution is open-source."
"We use the free version of Apache NiFi."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"They provide an open-source license for the on-premise version."
"We are using the free version of the solution."
"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."
"Spark is an open-source solution, so there are no licensing costs."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
15%
Manufacturing Company
8%
Retailer
6%
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
8%
University
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What needs improvement with Apache NiFi?
The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases.
What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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...
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...
 

Comparisons

 

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Overview

 

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
Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.