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."The products comes with REST APIs."
"Implementing the main requirements regarding my support portal."
"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 feature for us is the analytics that we can configure and view using Kibana."
"The most valuable feature of Elastic Enterprise Search is the Discovery option for the visualization of logs on a GPU instead of on the server."
"Dashboard is very customizable."
"The most valuable features are the ease and speed of the setup."
"I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."
"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."
"It is a stable product."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"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."
"The interface is very good, and the algorithms are the very best."
"The UI point of view is not very powerful because it is dependent on Kibana."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"There is an index issue in which the data starts to crash as it increases."
"Elastic Enterprise Search can improve by adding some kind of search that can be used out of the box without too much struggle with configuration. With every kind of search engine, there is some kind of special function that you need to do. A simple out-of-the-box search would be useful."
"Elasticsearch could improve by honoring Unix environmental variables and not relying only on those provided by Java (e.g. installing plugins over the Unix http proxy)."
"This product could be improved with additional security, and the addition of support for machine learning devices."
"Dashboards could be more flexible, and it would be nice to provide more drill-down capabilities."
"They're making changes in their architecture too frequently."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"A few people said it became slow after a while."
"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."
"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 is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"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."
"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."
Elastic Search is ranked 1st in Indexing and Search with 59 reviews while Weka is ranked 2nd in Data Mining with 14 reviews. Elastic Search is rated 8.2, while Weka is rated 7.6. The top reviewer of Elastic Search writes "Played a crucial role in enhancing our cybersecurity efforts ". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". Elastic Search is most compared with Faiss, Milvus, Pinecone, Azure Search and Amazon Kendra, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, Oracle Advanced Analytics and SAS Analytics. See our Elastic Search vs. Weka report.
We monitor all Indexing and Search reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.