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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.
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.
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!
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.
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.
Pros of Amazon Kendra
Pros of Elasticsearch
- Powerful api327
- Great search engine315
- Open source230
- Restful214
- Near real-time search199
- Free97
- Search everything84
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Highly Available3
- Awesome, great tool3
- Great docs3
- Easy to scale3
- Fast2
- Easy setup2
- Great customer support2
- Intuitive API2
- Great piece of software2
- Reliable2
- Potato2
- Nosql DB2
- Document Store2
- Not stable1
- Scalability1
- Open1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Easy to get hot data1
- Community0
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Cons of Amazon Kendra
- Expensive3
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
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