We performed a comparison between Coralogix and Weka based on real PeerSpot user reviews.
Find out in this report how the two Anomaly Detection Tools solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Numerous data monitoring tools are available, but Coralogix somehow fine-tunes our policies and effectively supports our teams."
"The best feature of this solution allows us to correlate logs, metrics and traces."
"The initial setup is straightforward."
"The solution offers very good convenience filtering."
"A non-tech person can easily get used to it."
"The solution is easy to use and to start with."
"It is a stable product."
"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."
"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."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"It would be helpful if Coralogix could integrate the main modules that any organization requires into a single subscription."
"The documentation of the tool could be improved"
"The user interface could be more intuitive and explanatory."
"Maybe they could make it more user-friendly."
"From my experience, Coralogix has horrible Terraform providers."
"We want it to work at what it is expected to work at and not really based on the updated configuration which one developer has decided to change."
"Not particularly user friendly."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"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."
"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."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
Coralogix is ranked 3rd in Anomaly Detection Tools with 7 reviews while Weka is ranked 2nd in Anomaly Detection Tools with 14 reviews. Coralogix is rated 8.4, while Weka is rated 7.6. The top reviewer of Coralogix writes "Good capabilities, has a helpful interface and is straightforward to set up". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". Coralogix is most compared with Datadog, Grafana, Sentry, New Relic and Elastic Search, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, Oracle Advanced Analytics and SAS Analytics. See our Coralogix vs. Weka report.
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