Amazon Kendra vs Elasticsearch

Overview

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Amazon Kendra

Stacks53

Followers143

Votes0

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.

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.
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.
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.
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.
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.
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.

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Advice on Elasticsearch, Amazon Kendra

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

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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Comments

Detailed Comparison

Elasticsearch	Amazon Kendra
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).	It is a highly accurate and easy to use enterprise search service that’s 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.
Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License	Natural language & keyword support; Reading comprehension & FAQ matching; Document ranking; Connectors; Relevance tuning; Domain optimization
Statistics
Stacks 35.5K	Stacks 53
Followers 27.1K	Followers 143
Votes 1.6K	Votes 0
Pros & Cons
Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale	Cons 3 Expensive
Integrations
Kibana Beats Logstash	Dropbox Bootstrap React AWS IAM Amazon VPC Box Microsoft SharePoint Amazon RDS TypeScript Salesforce Sales Cloud

What are some alternatives to Elasticsearch, Amazon Kendra?

Algolia

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.

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

Swiftype

Swiftype is the easiest way to add great search to your website or mobile application.

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

Bonsai

Your customers expect fast, near-magical results from your search. Help them find what they’re looking for with Bonsai Elasticsearch. Our fully managed Elasticsearch solution makes it easy to create, manage, and test your app's search.

Related Comparisons

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.

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.
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.
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.
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.
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.
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.

Amazon Kendra vs Elasticsearch

Overview