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 838,713 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

6.8

Number of Reviews

Ranking in other categories

Indexing and Search (1st), Cloud Data Integration (11th), Search as a Service (1st), Vector Databases (1st)

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 27.8%, up 25.7% compared to last year.
Weka, on the other hand, focuses on Data Mining, holds 21.2% mindshare, up 20.1% 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

"ELK Elasticsearch is 100% scalable as scalability is built into the design"

"We can easily collect all the data and view historical trends using the product. We can view the applications and identify the issues effectively."

"The most valuable features are its user-friendly interface and seamless navigation."

"It provides deep visibility into your cloud and distributed applications, from microservices to serverless architectures. It quickly identifies and resolves the root causes of issues, like gaining visibility into all the cloud-based and on-prem applications."

"Elastic Search makes handling large data volumes efficient and supports complex search operations."

"The solution offers good stability."

"The solution is valuable for log analytics."

"It is easy to scale with the cluster node model."

More Elastic Search pros

"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 is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result."

"It doesn’t cost anything to use the product."

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

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

"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."

"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."

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

More Weka pros

Cons

"There is a maximum of 10,000 entries, so the limitation means that if I wanted to analyze certain IP addresses more than 10,000 times, I wouldn't be able to dump or print that information."

"The pricing of this product needs to be more clear because I cannot understand it when I review the website."

"There are potential improvements based on our client feedback, like unifying the licensing cost structure."

"Elasticsearch could be improved in terms of scalability."

"There should be more stability."

"They're making changes in their architecture too frequently."

"Scalability and ROI are the areas they have to improve."

"We'd like to see more integration in the future, especially around service desks or other ITSM tools."

More Elastic Search cons

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

"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."

"Weka could be more stable."

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

"If there are a lot more lines of code, then we should use another language."

"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."

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

"Not particularly user friendly."

More Weka cons

Pricing and Cost Advice

"The basic license is free, but it comes with a lot of features that aren't free. With a gold license, we get active directory integration. With a platinum license, we get alerting."

"We are using the open-sourced version."

"Although the ELK Elasticsearch software is open-source, we buy the hardware."

"The tool is an open-source product."

"The tool is not expensive. Its licensing costs are yearly."

"The premium license is expensive."

"An X-Pack license is more affordable than Splunk."

"The solution is free."

More Elastic Search pricing and cost advice

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

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

"The solution is free and open-source."

"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

18%

Financial Services Firm

15%

Manufacturing Company

Government

University

20%

Educational Organization

14%

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

Comparisons

Milvus vs Elastic Search

Compared 16% of the time

Faiss vs Elastic Search

Compared 13% of the time

Compared 7% of the time

Azure Search vs Elastic Search

Compared 6% of the time

Qdrant vs Elastic Search

Compared 5% of the time

More Elastic Search Competitors

KNIME vs Weka

Compared 63% of the time

IBM SPSS Statistics vs Weka

Compared 16% of the time

IBM SPSS Modeler vs Weka

Compared 6% of the time

Splunk User Behavior Analytics vs Weka

Compared 6% of the time

More Weka Competitors

Product Reports

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Elastic Search

February 2025

Download Weka product report

January 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.

Information Not Available