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Apache Hadoop vs Oracle Big Data Appliance comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 2024

Review summaries and opinions

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

Categories and Ranking

Apache Hadoop
Ranking in Data Warehouse
8th
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
40
Ranking in other categories
No ranking in other categories
Oracle Big Data Appliance
Ranking in Data Warehouse
19th
Average Rating
8.0
Reviews Sentiment
7.5
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Data Warehouse category, the mindshare of Apache Hadoop is 4.9%, down from 5.1% compared to the previous year. The mindshare of Oracle Big Data Appliance is 1.4%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Warehouse
 

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.
Mohammed Hamad - PeerSpot reviewer
Provides clean, centralized data
From a technical perspective, Big Data Appliance could be improved with more innovation in the AI and machine-learning parts instead of relying on Cloudera. Oracle could also improve Big Data Appliance by having one technology on their stack and working on it instead of continually changing the name or technologies or features. In addition, they could have a program to enable their partners to use this technology because right now, I have to have an expert to use the AI elements.

Quotes from Members

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

Pros

"The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable."
"I recommend it for the telecom sector. I know it well, and it's a good fit."
"Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources."
"The tool's stability is good."
"We selected Apache Hadoop because it is not dependent on third-party vendors."
"The best thing about this solution is that it is very powerful and very cheap."
"The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics."
"The scalability of Apache Hadoop is very good."
"Because Big Data Appliance allows me to have a single source of truth, it means I have clean data that can be monetized and leveraged to gain more insights with real-time reports from the dashboard."
"This is a comprehensive solution that is easy to deploy."
"The best thing about the product is that the end-user can build the reports by themselves without really knowing anything about databases."
 

Cons

"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."
"The load optimization capabilities of the product are an area of concern where improvements are required."
"The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment."
"Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them."
"The upgrade path should be improved because it is not as easy as it should be."
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error."
"The solution is very expensive."
"From a technical perspective, Big Data Appliance could be improved with more innovation in the AI and machine-learning parts instead of relying on Cloudera."
"It seems like the deployment of repositories has become more difficult in later versions of the product rather than easier."
"The product should be simplified for the average user."
 

Pricing and Cost Advice

"We don't directly pay for it. Our clients pay for it, and they usually don't complain about the price. So, it is probably acceptable."
"This is a low cost and powerful solution."
"For any big enterprise the costs can be handled, and it is suitable for big enterprises because the scale of data is large. For medium and small enterprises, the tool is on the high-price side."
"​There are no licensing costs involved, hence money is saved on the software infrastructure​."
"It's reasonable, but there's room for improvement in cost-effectiveness."
"The price could be better. Hortonworks no longer exists, and Cloudera killed the free version of Hadoop."
"We just use the free version."
"The price of Apache Hadoop could be less expensive."
"Oracle's prices are too high compared to others in the market."
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Top Industries

By visitors reading reviews
Financial Services Firm
34%
Computer Software Company
12%
University
5%
Energy/Utilities Company
5%
Financial Services Firm
31%
Computer Software Company
15%
Government
8%
Manufacturing Company
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.
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...
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...
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Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
Caixa Bank
Find out what your peers are saying about Apache Hadoop vs. Oracle Big Data Appliance and other solutions. Updated: May 2025.
860,168 professionals have used our research since 2012.