Elastic Search vs Weka comparison

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2,264 views|729 comparisons
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Read 14 Weka reviews
3,732 views|1,766 comparisons
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).
765,386 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
"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."

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"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."

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Cons
"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."

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"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."

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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:Data indexing of historical data is the most beneficial feature of the product.
    Top Answer:I use the community version. The premium license is expensive. I rate the tool’s pricing an eight out of ten.
    Top Answer:The solution must provide AI integrations. I could direct my data flow to my AI tools if I use Elastic for IoT data.
    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
    2,264
    Comparisons
    729
    Reviews
    27
    Average Words per Review
    512
    Rating
    8.3
    2nd
    out of 18 in Data Mining
    Views
    3,732
    Comparisons
    1,766
    Reviews
    7
    Average Words per Review
    518
    Rating
    7.9
    Comparisons
    Milvus logo
    Compared 12% of the time.
    Faiss logo
    Compared 11% of the time.
    Azure Search logo
    Compared 8% of the time.
    Amazon Kendra logo
    Compared 6% of the time.
    Pinecone logo
    Compared 5% of the time.
    KNIME logo
    Compared 61% 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%
    Government8%
    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 Business19%
    Midsize Enterprise18%
    Large Enterprise63%
    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.
    765,386 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 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.