Oracle Advanced Analytics vs Weka comparison

Oracle Advanced Analytics

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Executive SummaryUpdated on May 21, 2025

Oracle Advanced Analytics and Weka compete in data analysis tools. Oracle holds an advantage in large-scale enterprise data integration, while Weka excels with machine learning algorithms and flexibility.

Features: Oracle Advanced Analytics is known for enterprise-grade data management, predictive analytics, and robust security features. Weka offers a variety of machine learning algorithms, easy integration of new algorithms, and a strong open-source model allowing educational flexibility.

Room for Improvement: Oracle could enhance its deployment ease and integration beyond its ecosystem. Improved user interface design would be beneficial for Weka, along with enhanced data visualization and user support for enterprise solutions.

Ease of Deployment and Customer Service: Oracle requires substantial resources for deployment but provides comprehensive support services. Weka offers simple deployment with community-driven support, although it lacks formal customer service frameworks.

Pricing and ROI: Oracle has a higher setup cost, justified by scalability and potential ROI from business insights. Weka's open-source nature leads to lower setup costs, suitable for smaller budgets seeking flexible machine learning solutions.

To learn more, read our detailed Oracle Advanced Analytics vs. Weka Report (Updated: June 2025).

Oracle Advanced Analytics vs. Weka

June 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:

Categories and Ranking

Oracle Advanced Analytics

Ranking in Data Mining

6th

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Weka

Ranking in Data Mining

2nd

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Anomaly Detection Tools (4th)

Mindshare comparison

As of June 2025, in the Data Mining category, the mindshare of Oracle Advanced Analytics is 4.2%, down from 4.9% compared to the previous year. The mindshare of Weka is 18.2%, down from 20.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining

Featured Reviews

Alejandro Merida

Enterprise Solutions Architect at CONTPAQi

Has efficient predictive analysis capabilities, helping us maintain a secure network environment

Our primary use case for the product is managing and analyzing our business's data lakes, which have accumulated over nearly 40 years The platform's most valuable features are the efficient organization of data and the ability to extract specific insights for predictive analysis. One area for…

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AwaisAnwar

Treasury Management in Finance Department at National University of Pakistan

Open source, good for basic data mining use cases except for the visualization results

I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results. Moreover, a new user interface would be great, especially for beginners. Something that guides them through the available tools and helps them achieve their goals. I haven't seen anything like that myself, though maybe it's there and I missed it.

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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

"Oracle Advanced Analytics effectively prevents threats and helps maintain a secure network environment."

"When needed, we will work closely with Oracle support and implement their workaround in our application."

"Ability to pull together multiple sources of information."

"The dashboard interface is intuitive and the user is able to interact with it to receive good results from the analytic."

"Weka eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks."

"With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering."

"It is a stable product."

"I like the machine algorithm for clustering systems. Weka has larger capabilities. There are multiple algorithms that can be used for clustering. It depends upon the user requirements. For clustering, I've used DBSCAN, whereas for supervised learning, I've used AVM and RFT."

"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."

"There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way."

"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."

"The interface is very good, and the algorithms are the very best."

More Weka pros

Cons

"The performance, scalability and queries should be addressed, as well as the data distribution of certain data techniques."

"Could use some refinement getting things that are not standard cloud applications, but more customized."

"One area for product improvement is the user experience, particularly when navigating through screens."

"There are some transactions we have not been able to find through the dashboard."

"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated."

"Not particularly user friendly."

"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."

"The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable."

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."

"The product is good, but I would like it to work with big data. I know it has a Spark integration they could use to do analysis in clusters, but it's not so clear how to use it."

"The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together."

"I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning."

More Weka cons

Pricing and Cost Advice

"The solution is priced well."

"We use the free version now. My faculty is very small."

"The solution is free and open-source."

"Currently, I am using an open-source version so I don't know much about the price of this solution."

"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."

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Top Industries

By visitors reading reviews

Government

17%

Computer Software Company

15%

Educational Organization

13%

Manufacturing Company

13%

University

17%

Educational Organization

16%

Computer Software Company

11%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What needs improvement with Oracle Advanced Analytics?

One area for product improvement is the user experience, particularly when navigating through screens, especially on Windows.

What is your primary use case for Oracle Advanced Analytics?

Our primary use case for the product is managing and analyzing our business's data lakes, which have accumulated over nearly 40 years.

What advice do you have for others considering Oracle Advanced Analytics?

Oracle Advanced Analytics effectively prevents threats and helps maintain a secure network environment. Experienced personnel are crucial for handling the solution effectively after installation. O...

What do you like most about Weka?

It is a stable product.

What is your experience regarding pricing and costs for Weka?

Weka is free and open-source software. That is why I used it over KNIME.

What needs improvement with Weka?

IBM SPSS Statistics vs Oracle Advanced Analytics

Comparisons

Compared 34% of the time

SAS Analytics vs Oracle Advanced Analytics

Compared 25% of the time

KNIME vs Oracle Advanced Analytics

Compared 20% of the time

More Oracle Advanced Analytics Competitors

KNIME vs Weka

Compared 63% of the time

IBM SPSS Statistics vs Weka

Compared 15% of the time

IBM SPSS Modeler vs Weka

Compared 8% of the time

SAS Analytics vs Weka

Compared 5% of the time

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Product Reports

Oracle Advanced Analytics

May 2025

Download Oracle Advanced Analytics product report

Download Weka product report

May 2025

Also Known As

OAA

No data available

Overview

Oracle Advanced Analytics 12c delivers parallelized in-database implementations of data mining algorithms and integration with open source R. Data analysts use Oracle Data Miner GUI and R to build and evaluate predictive models and leverage R packages and graphs. Application developers deploy Oracle Advanced Analytics models using SQL data mining functions and R. With the Oracle Advanced Analytics option, Oracle extends the Oracle Database to an sclable analytical platform that mines more data and data types, eliminates data movement, and preserves security to anticipate customer behavior, detect patterns, and deliver actionable insights. Oracle Big Data SQL adds new big data sources and Oracle R Advanced Analytics for Hadoop provides algorithms that run on Hadoop.

Oracle

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Sample Customers

Orbitz, Marriott, SGS Life Science, Masdar, AlliantEnergy Corporation, British Standards Institute, Skybox Security, Triple PointTechnology, and Coca Cola.

Information Not Available