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 forced merge and forced resonate features reduce the data size increasing reliability."
"The solution has great scalability."
"The UI is very nice, and performance wise it's quite good too."
"I value the feature that allows me to share the dashboards to different people with different levels of access."
"It is highly valuable because of its simplicity in maintenance, where most tasks are handled for you, and it offers a plethora of built-in features."
"It gives us the possibility to store and query this data and also do this efficiently and securely and without delays."
"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."
"A good use case is saving metadata of your systems for data cataloging. Various systems, like those opened in metadata and similar applications, use Elasticsearch to store their text 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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"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."
"It is a stable product."
"It doesn’t cost anything to use the product."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"Elastic Enterprise Search's tech support is good but it could be improved."
"The pricing of this product needs to be more clear because I cannot understand it when I review the website."
"The metadata gets stored along with indexes and isn't queryable."
"While integrating with tools like agents for ingesting data from sources like firewalls is valuable, I believe prioritizing improvements to the core product would be more beneficial."
"Elastic Search needs to improve its technical support. It should be customer-friendly and have good support."
"The one area that can use improvement is the automapping of fields."
"I would like to see more integration for the solution with different platforms."
"Could have more open source tools and testing."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"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."
"Not particularly user friendly."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"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."
"Weka could be more stable."
"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."
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, Azure Search, Pinecone 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.
