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."It is stable."
"I appreciate that Elastic Enterprise Search is easy to use and that we have people on our team who are able to manage it effectively."
"ELK Elasticsearch is 100% scalable as scalability is built into the design"
"It helps us to analyse the logs based on the location, user, and other log parameters."
"The most valuable features are the data store and the X-pack extension."
"The ability to aggregate log and machine data into a searchable index reduces time to identify and isolate issues for an application. Saves time in triage and incident response by eliminating manual steps to access and parse logs on separate systems, within large infrastructure footprints."
"I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."
"The solution has great scalability."
"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 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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"I would like to see more integration for the solution with different platforms."
"I have not been using the solution for many years to know exactly the improvements needed. However, they could simplify how the YML files have to be structured properly."
"The metadata gets stored along with indexes and isn't queryable."
"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)."
"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)."
"There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone."
"I would rate the stability a seven out of ten. We faced a few issues."
"Elastic Search could benefit from a more user-friendly onboarding process for beginners."
"If there are a lot more lines of code, then we should use another language."
"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."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"A few people said it became slow after a while."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
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.
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