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 special text processing features in this solution are very important for me."
"The most valuable feature for us is the analytics that we can configure and view using Kibana."
"I am impressed with the product's Logstash. The tool is fast and customizable. You can build beautiful dashboards with it. It is useful and reliable."
"It helps us to analyse the logs based on the location, user, and other log parameters."
"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 search speed is most valuable and important."
"Dashboard is very customizable."
"The most valuable features are the detection and correlation features."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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 doesn’t cost anything to use the product."
"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."
"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."
"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."
"There are a lot of manual steps on the operating system. It could be simplified in the user interface."
"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."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"Could have more open source tools and testing."
"I would like to be able to do correlations between multiple indexes."
"It needs email notification, similar to what Logentries has. Because of the notification issue, we moved to Logentries, as it provides a simple way to receive notification whenever a server encounters an error or unexpected conditions (which we have defined using RegEx)."
"Ratio aggregation is not supported in this solution."
"I would like to see more integration for the solution with different platforms."
"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."
"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."
"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."
"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 is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"If there are a lot more lines of code, then we should use another language."
"Not particularly user friendly."
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 Milvus, Faiss, Azure Search, Amazon Kendra and Pinecone, 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.