Elastic Search vs Weka comparison

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Elastic Logo
2,186 views|735 comparisons
98% willing to recommend
Weka Logo
Read 14 Weka reviews
3,678 views|1,726 comparisons
78% willing to recommend
Comparison Buyer's Guide
Executive Summary

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.
To learn more, read our detailed Elastic Search vs. Weka Report (Updated: January 2022).
768,578 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"Gives us a more user-friendly, centralized solution (for those who just needed a quick glance, without being masters of sed and awk) as well as the ability to implement various mechanisms for machine-learning from our logs, and sending alerts for anomalies.""Elasticsearch includes a graphical user interface (GUI) called Kibana. The GUI features are extremely beneficial to us.""The solution offers good stability.""The solution has great scalability.""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 observability is the best available because it provides granular insights that identify reasons for defects.""Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time analytics with Elastic benefits us due to the huge traffic volume in our organization, which reaches up to 60,000 requests per second. With logs of approximately 25 GB per day, manually analyzing traffic behavior, payloads, headers, user agents, and other details is impractical.""The forced merge and forced resonate features reduce the data size increasing reliability."

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"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.""Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data.""It is a stable product.""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.""I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka.""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.""It doesn’t cost anything to use the product.""Working with complicated algorithms in huge datasets is really easy in Weka."

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Cons
"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.""We have an issue with the volume of data that we can handle.""I would like to see more integration for the solution with different platforms.""The documentation regarding customization could be better.""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.""There are a lot of manual steps on the operating system. It could be simplified in the user interface.""It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement.""They could improve some of the platform's infrastructure management capabilities."

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"If there are a lot more lines of code, then we should use another language.""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.""Not particularly user friendly.""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.""Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science.""Weka could be more stable.""While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results.""In terms of scalability, I think Weka is not prepared to handle a large number of users."

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Pricing and Cost Advice
  • "ELK has been considered as an alternative to Splunk to reduce licensing costs."
  • "An X-Pack license is more affordable than Splunk."
  • "​The pricing and license model are clear: node-based model."
  • "This is a free, open source software (FOSS) tool, which means no cost on the front-end. There are no free lunches in this world though. Technical skill to implement and support are costly on the back-end with ELK, whether you train/hire internally or go for premium services from Elastic."
  • "We are using the free version and intend to upgrade."
  • "It can be expensive."
  • "This product is open-source and can be used free of charge."
  • "We are using the open-sourced version."
  • More Elastic Search Pricing and Cost Advice →

  • "Currently, I am using an open-source version so I don't know much about the price of this solution."
  • "The solution is free and open-source."
  • "As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."
  • "We use the free version now. My faculty is very small."
  • More Weka Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time… more »
    Top Answer:I don't see improvements at the moment. The current setup is working well for me, and I'm satisfied with it. Integrating with different platforms is also fine, and I'm not recommending any changes or… more »
    Top Answer:Weka is free and open-source software. That is why I used it over KNIME.
    Top Answer:I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding… more »
    Ranking
    1st
    out of 25 in Indexing and Search
    Views
    2,186
    Comparisons
    735
    Reviews
    27
    Average Words per Review
    501
    Rating
    8.3
    2nd
    out of 18 in Data Mining
    Views
    3,678
    Comparisons
    1,726
    Reviews
    7
    Average Words per Review
    518
    Rating
    7.9
    Comparisons
    Faiss logo
    Compared 14% of the time.
    Milvus logo
    Compared 13% of the time.
    Azure Search logo
    Compared 7% of the time.
    Pinecone logo
    Compared 6% of the time.
    Amazon Kendra logo
    Compared 6% of the time.
    KNIME logo
    Compared 59% of the time.
    IBM SPSS Statistics logo
    Compared 15% of the time.
    IBM SPSS Modeler logo
    Compared 7% of the time.
    Oracle Advanced Analytics logo
    Compared 6% of the time.
    SAS Analytics logo
    Compared 5% of the time.
    Also Known As
    Elastic Enterprise Search, Swiftype, Elastic Cloud
    Learn More
    Weka
    Video Not Available
    Overview

    Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

    Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

    Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

    At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

    Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

    In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.

    Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
    Sample Customers
    T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.
    Information Not Available
    Top Industries
    REVIEWERS
    Financial Services Firm33%
    Computer Software Company27%
    Manufacturing Company10%
    Insurance Company7%
    VISITORS READING REVIEWS
    Computer Software Company18%
    Financial Services Firm15%
    Government7%
    Manufacturing Company7%
    VISITORS READING REVIEWS
    University18%
    Educational Organization14%
    Computer Software Company10%
    Comms Service Provider7%
    Company Size
    REVIEWERS
    Small Business41%
    Midsize Enterprise11%
    Large Enterprise48%
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise13%
    Large Enterprise63%
    REVIEWERS
    Small Business70%
    Midsize Enterprise10%
    Large Enterprise20%
    VISITORS READING REVIEWS
    Small Business20%
    Midsize Enterprise18%
    Large Enterprise62%
    Buyer's Guide
    Elastic Search vs. Weka
    January 2022
    Find out what your peers are saying about Elastic Search vs. Weka and other solutions. Updated: January 2022.
    768,578 professionals have used our research since 2012.

    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.