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Apache Hadoop vs Apache Spark comparison

The compared Apache Hadoop and Apache Spark solutions aren't in the same category. Apache Hadoop is ranked #8 in Data Warehouse , with an average rating of 8.0, and holds a 4.8% mindshare in the category. Apache Spark is ranked #1 in H , with an average rating of 8.6, and holds a 19.2% mindshare. Additionally, 89% of Apache Hadoop users are willing to recommend the solution, compared to 90% of Apache Spark users who would recommend it.
Apache Hadoop
Read 40 Apache Hadoop reviews
  • 1,940 Views
  • 1,028 Comparison Views
89% willing to recommend
Apache Spark
Read 67 Apache Spark reviews
  • 4,468 Views
  • 983 Comparison Views
90% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Apache Hadoop and Apache Spark based on real PeerSpot user reviews.

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Buyer's Guide
Data Warehouse
July 2025
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Review summaries and opinions

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

ROI

Sentiment score
6.5
Apache Hadoop provides cost-effective data storage and processing, though ROI varies based on analytics use and sophistication.
Sentiment score
6.6
Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.
 

Customer Service

Sentiment score
6.4
Customer service varies by Hadoop distributor, with Hortonworks rated highly; support depends on vendor, community resources, or external vendors.
Sentiment score
5.9
Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
For more quotes and insights, download the Apache Hadoop report
No quotes available
For more quotes and insights, download the Apache Spark report
 

Scalability Issues

Sentiment score
7.6
Apache Hadoop excels in scalability, allowing easy cluster expansion and efficient data handling, ideal for varied organizational needs.
Sentiment score
7.5
Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
For more quotes and insights, download the Apache Hadoop report
No quotes available
For more quotes and insights, download the Apache Spark report
 

Stability Issues

Sentiment score
7.3
Apache Hadoop's stability, rated 8/10, improves with newer versions, though minor issues exist with memory and data processing.
Sentiment score
7.5
Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
For more quotes and insights, download the Apache Hadoop report
MapReduce needs to perform numerous disk input and output operations, while Apache Spark can use memory to store and process data.
For more quotes and insights, download the Apache Spark report
 

Room For Improvement

Apache Hadoop needs improved usability, integration, security, support, and performance for efficient high-volume data processing and better community resources.
Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
For more quotes and insights, download the Apache Hadoop report
No quotes available
For more quotes and insights, download the Apache Spark report
 

Setup Cost

Enterprise Hadoop offers cost benefits but varies with deployment type and distribution, impacting smaller organizations more heavily.
Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.
 

Valuable Features

Apache Hadoop excels with a scalable, cost-effective system handling diverse data types, integrating with tools, and supporting big data analytics.
Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
For more quotes and insights, download the Apache Hadoop report
Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.
For more quotes and insights, download the Apache Spark report
 

Categories and Ranking

Apache Hadoop
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
40
Ranking in other categories
Data Warehouse (8th)
Apache Spark
Average Rating
8.4
Reviews Sentiment
7.3
Number of Reviews
67
Ranking in other categories
Hadoop (1st), Compute Service (4th), Java Frameworks (2nd)
 

Mindshare comparison

Apache Hadoop and Apache Spark aren’t in the same category and serve different purposes. Apache Hadoop is designed for Data Warehouse and holds a mindshare of 4.8%, down 4.9% compared to last year.
Apache Spark, on the other hand, focuses on Hadoop, holds 19.2% mindshare, down 20.2% since last year.
Data Warehouse
Hadoop
 

Q&A Highlights

it_user1272297 - PeerSpot reviewer
Apr 19, 2020
Which is the best RDMBS solution for big data?
I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505
See all answers
 

Featured Reviews

Sushil Arya - PeerSpot reviewer
Provides ease of integration with the IT workflow of a business
When working with Kafka, I saw that the data came in an incremental order. The incremental data processing part is still not very effective in Apache Hadoop. If the data is already there, it can be processed very effectively, especially if the data is coming in every second. If you want to know the location of some data every second, then such data is not processed effectively in Apache Hadoop. I can say that one of the features where improvements are required revolves around the licensing cost of the tool. If the tool can build some licensing structures in a pay-per-use manner, organizations can get the look and feel of Apache Hadoop. Apache Hadoop can offer a licensing structure of the product that can be seen as similar to how AWS operates. Apache Hadoop can look into the capability of processing incremental data. The tool's setup process can be a scope of improvement. Also, it is not very simple because while doing the setup, we need to do all the server settings, including port listing and firewall configurations. If we look at other products on the market, then they can be made simpler. There are certain shortcomings when it comes to the product's technical support part, making it an area where improvements are required. The time frame for the resolution is an area that needs to be improved. The overall communication part of the technical support team also needs improvement.
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Omar Khaled - PeerSpot reviewer
Empowering data consolidation and fast decision-making with efficient big data processing
I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.
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Answers from the Community

it_user1272297 - PeerSpot reviewer
Apr 19, 2020
Apr 19, 2020
Which is the best RDMBS solution for big data?
I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505
2 out of 4 answers
Russell Rothstein - PeerSpot reviewer
Jan 27, 2020
Morten, the most popular comparisons of SQream can be found here: https://www.itcentralstation.com/products/sqream-db-alternatives-and-competitors The top ones include Cassandra, MemSQL, MongoDB, and Vertica.
Read full answer
reviewer1219965 - PeerSpot reviewer
Jan 27, 2020
I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505
Read full answer
See all 4 answers
 

Top Industries

By visitors reading reviews
Financial Services Firm
35%
Computer Software Company
11%
University
6%
Energy/Utilities Company
5%
Financial Services Firm
26%
Computer Software Company
10%
Manufacturing Company
7%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Hadoop?
It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming.
See all answers
What is your experience regarding pricing and costs for Apache Hadoop?
The product is open-source, but some associated licensing fees depend on the subscription level. While it might be free for students, organizations typically need to pay for their subscriptions. Th...
See all answers
What needs improvement with Apache Hadoop?
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it. This wa...
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?
Apache Spark is open-source, so it doesn't incur any charges.
See all answers
What needs improvement with Apache Spark?
There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...
See all answers
 

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Product Reports

Buyer's Guide
Apache Hadoop
August 2025
Apache Hadoop reportDownload Apache Hadoop product report
Buyer's Guide
Apache Spark
August 2025
Apache Spark reportDownload Apache Spark product report
 

Overview

The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Apache

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

Apache
 

Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
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
Data Warehouse
July 2025
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