Elastic Search vs Weka comparison

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Elastic Search

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Executive Summary

We performed a comparison between Elastic Search and Weka based on real PeerSpot user reviews.

Find out in this report how the two Indexing and Search solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Elastic Search vs. Weka Report (Updated: January 2022).

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Elastic Search vs. Weka

January 2022

Helped 842,592 peers since 2012

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

Elastic Search

Average Rating

8.2

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Indexing and Search (1st), Cloud Data Integration (10th), Search as a Service (1st), Vector Databases (2nd)

Weka

Average Rating

7.6

Number of Reviews

Ranking in other categories

Data Mining (2nd), Anomaly Detection Tools (4th)

Mindshare comparison

Elastic Search and Weka aren’t in the same category and serve different purposes. Elastic Search is designed for Indexing and Search and holds a mindshare of 26.3%, up 26.2% compared to last year.
Weka, on the other hand, focuses on Data Mining, holds 21.1% mindshare, up 20.4% since last year.

Indexing and Search

Data Mining

Featured Reviews

Anand_Kumar

Enterprise Architect at DXC Technology

Captures data from all other sources and becomes a MOM aka monitoring of monitors

Scalability and ROI are the areas they have to improve. Their license terms are based on the number of cores. If you increase the number of cores, it becomes very difficult to manage at a large scale. For example, if I have a $3 million project, I won't sell it because if we're dealing with a 10 TB or 50 TB system, there are a lot of systems and applications to monitor, and I have to make an MOM (Mean of Max) for everything. This is because of the cost impact. Also, when you have horizontal scaling, it's like a multi-story building with only one elevator. You have to run around, and it's not efficient. Even the smallest task becomes difficult. That's the problem with horizontal scaling. They need to improve this because if they increase the cores and adjust the licensing accordingly, it would make more sense.

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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

"Implementing the main requirements regarding my support portal."

"We had many reasons to implement Elasticsearch for search term solutions. Elasticsearch products provide enterprise landscape support for different areas of the company."

"Elasticsearch includes a graphical user interface (GUI) called Kibana. The GUI features are extremely beneficial to us."

"The search speed is most valuable and important."

"I value the feature that allows me to share the dashboards to different people with different levels of access."

"The flexibility and the support for diverse languages that it provides for searching the database are most valuable. We can use different languages to query the database."

"The tool's stability and performance are good."

"The ability to aggregate log and machine data into a searchable index reduces time to identify and isolate issues for an application. Saves time in triage and incident response by eliminating manual steps to access and parse logs on separate systems, within large infrastructure footprints."

More Elastic Search pros

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

"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."

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

"The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data."

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

"Working with complicated algorithms in huge datasets is really easy in Weka."

"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."

"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."

More Weka pros

Cons

"I would rate the stability a seven out of ten. We faced a few issues."

"There is a lack of technical people to develop, implement and optimize equipment operation and web queries."

"Elasticsearch could be improved in terms of scalability."

"The GUI is the part of the program which has the most room for improvement."

"Enterprise scaling of what have been essentially separate, free open source software (FOSS) products has been a challenge, but the folks at Elastic have published new add-ons (X-Pack and ECE) to help large companies grow ELK to required scales."

"Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one." In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release."

"I don't see improvements at the moment. The current setup is working well for me, and I'm satisfied with it. Integrating with different platforms is also fine, and I'm not recommending any changes or enhancements right now."

"Dashboards could be more flexible, and it would be nice to provide more drill-down capabilities."

More Elastic Search cons

"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 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."

"Weka could be more stable."

"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."

"In terms of scalability, I think Weka is not prepared to handle a large number of users."

"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."

"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."

"A few people said it became slow after a while."

More Weka cons

Pricing and Cost Advice

"This product is open-source and can be used free of charge."

"The solution is affordable."

"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive."

"The cost varies based on factors like usage volume, network load, data storage size, and service utilization. If your usage isn't too extensive, the cost will be lower."

"The pricing model is questionable and needs to be addressed because when you would like to have the security they charge per machine."

"There is a free version, and there is also a hosted version for which you have to pay. We're currently using the free version. If things go well, we might go for the paid version."

"Elastic Search is open-source, but you need to pay for support, which is expensive."

"We are using the open-sourced version."

More Elastic Search pricing and cost advice

"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."

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

"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

Computer Software Company

17%

Financial Services Firm

15%

Government

Manufacturing Company

University

19%

Educational Organization

15%

Computer Software Company

11%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about ELK Elasticsearch?

Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time anal...

What is your experience regarding pricing and costs for ELK Elasticsearch?

I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.

What needs improvement with ELK Elasticsearch?

I found an issue with Elasticsearch in terms of aggregation. They are good, yet the rules written for this are not really good. There is a maximum of 10,000 entries, so the limitation means that if...

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?

Pinecone vs Elastic Search

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Compared 17% of the time

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Compared 7% of the time

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Product Reports

Download Elastic Search product report

Elastic Search

March 2025

Download Weka product report

March 2025

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud

No data available

Overview

Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.

Elastic

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

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.

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