"The most valuable features are the ease and speed of the setup."
"The initial setup is very easy for small environments."
"Dashboard is very customizable."
"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 most valuable feature is the out of the box Kibana."
"The solution has good security features. I have been happy with the dashboards and interface."
"The solution is quite scalable and this is one of its advantages."
"You have dashboards, it is visual, there are maps, you can create canvases. It's more visual than anything that I've ever used."
"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."
"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."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"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."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"Something that could be improved is better integrations with Cortex and QRadar, for example."
"I would like to be able to do correlations between multiple indexes."
"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."
"Improving machine learning capabilities would be beneficial."
"The solution has quite a steep learning curve. The usability and general user-friendliness could be improved. However, that is kind of typical with products that have a lot of flexibility, or a lot of capabilities. Sometimes having more choices makes things more complex. It makes it difficult to configure it, though. It's kind of a bitter pill that you have to swallow in the beginning and you really have to get through it."
"The metadata gets stored along with indexes and isn't queryable."
"The different applications need to be individually deployed."
"There is an index issue in which the data starts to crash as it increases."
"If there are a lot more lines of code, then we should use another language."
"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."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"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."
"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."
"Not particularly user friendly."
"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."
Earn 20 points
ELK Elasticsearch is ranked 1st in Anomaly Detection Tools with 19 reviews while Weka is ranked 3rd in Anomaly Detection Tools with 7 reviews. ELK Elasticsearch is rated 8.0, while Weka is rated 7.2. The top reviewer of ELK Elasticsearch writes "Good processing power, very scalable, and able to handle all data formats". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". ELK Elasticsearch is most compared with Amazon Athena, Azure Search, Splunk User Behavior Analytics, Amazon AWS CloudSearch and Anodot, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, SAS Analytics and IBM Smart Analytics. See our ELK Elasticsearch vs. Weka report.
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