Amazon Kendra聽vs聽Elasticsearch

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Amazon Kendra

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Amazon Kendra vs Elasticsearch: What are the differences?

Amazon Kendra and Elasticsearch are both search solutions designed to help organizations index and search their data efficiently. Let's explore the key differences between them.

  1. Querying Capabilities: Amazon Kendra is designed for natural language querying, making it easy to retrieve information using conversational queries. Elasticsearch, on the other hand, provides powerful full-text search capabilities and supports complex query structures, including Boolean and wildcard queries.

  2. Data Sources: Amazon Kendra is specifically designed for enterprise search and integrates seamlessly with various data sources, including databases, file systems, and SharePoint. Elasticsearch, on the other hand, is a distributed search and analytics engine that can handle data from various sources, including log files, NoSQL databases, and social media feeds.

  3. Managed Service: Amazon Kendra is a fully managed service, meaning that Amazon takes care of infrastructure management, scaling, and updates. Elasticsearch, on the other hand, can be self-managed or used as a managed service through Elasticsearch Service or Elastic Cloud.

  4. Natural Language Processing: Amazon Kendra leverages machine learning and natural language processing capabilities to understand user queries, extract relevant information, and provide accurate search results. Elasticsearch, however, does not have built-in natural language processing and requires additional configurations or integrations to perform similar tasks.

  5. Indexing and Data Ingestion: Amazon Kendra provides a simplified process for data indexing and ingestion, including automatic document metadata extraction and enriched search results. Elasticsearch offers more flexibility and customization options for indexing data, making it suitable for complex data structures and advanced data analysis.

  6. Scalability and Performance: Amazon Kendra is built on top of highly scalable and performant infrastructure, allowing it to handle large volumes of data and concurrent user queries effectively. Elasticsearch is designed to be horizontally scalable, allowing it to handle massive amounts of data and provide near-real-time search and analytics capabilities.

In summary, Amazon Kendra is a managed service with natural language querying capabilities, specifically designed for enterprise search and seamless integration with various data sources. Elasticsearch, on the other hand, provides powerful full-text search capabilities, flexibility in data sources and indexing, and can be self-managed or used as a managed service.

Advice on Amazon Kendra and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza | 6 upvotes 路 376.5K views
Needs advice

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit | 8 upvotes 路 280.6K views

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Amazon Kendra
Pros of Elasticsearch
    Be the first to leave a pro
    • 328
      Powerful api
    • 315
      Great search engine
    • 231
      Open source
    • 214
    • 200
      Near real-time search
    • 98
    • 85
      Search everything
    • 54
      Easy to get started
    • 45
    • 26
    • 6
      Fast search
    • 5
      More than a search engine
    • 4
      Great docs
    • 4
      Awesome, great tool
    • 3
      Highly Available
    • 3
      Easy to scale
    • 2
    • 2
      Document Store
    • 2
      Great customer support
    • 2
      Intuitive API
    • 2
      Nosql DB
    • 2
      Great piece of software
    • 2
    • 2
    • 2
      Easy setup
    • 1
    • 1
      Easy to get hot data
    • 1
    • 1
    • 1
      Actively developing
    • 1
      Responsive maintainers on GitHub
    • 1
    • 1
      Not stable
    • 1
    • 0

    Sign up to add or upvote prosMake informed product decisions

    Cons of Amazon Kendra
    Cons of Elasticsearch
    • 3
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
    • 4
      Hard to keep stable at large scale

    Sign up to add or upvote consMake informed product decisions

    What is Amazon Kendra?

    It is a highly accurate and easy to use enterprise search service that鈥檚 powered by machine learning. It delivers powerful natural language search capabilities to your websites and applications so your end users can more easily find the information they need within the vast amount of content spread across your company.

    What is Elasticsearch?

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

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    Blog Posts

    May 21 2019 at 12:20AM


    What are some alternatives to Amazon Kendra and Elasticsearch?
    Azure Cognitive Search
    It is the only cloud search service with built-in AI capabilities that enrich all types of information to easily identify and explore relevant content at scale. Formerly known as Azure Search, it uses the same integrated Microsoft natural language stack that Bing and Office have used for more than a decade and AI services across vision, language and speech. Spend more time innovating and less time maintaining a complex cloud search solution.
    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together.
    See all alternatives