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Elasticsearch vs Rekognition API: What are the differences?
## Introduction
Elasticsearch and Rekognition API are two popular tools used for different purposes in the field of data management and image processing respectively. Below are the key differences between Elasticsearch and Rekognition API.
1. **Functionality:** Elasticsearch is a search and analytics engine that is ideal for indexing and searching large volumes of data quickly and efficiently. On the other hand, Rekognition API is a cloud-based image analysis service that can easily recognize objects, scenes, and faces in images and videos.
2. **Use Case:** Elasticsearch is commonly used for real-time data analytics, log monitoring, and full-text search capabilities in applications. In contrast, Rekognition API is mainly used for content moderation, facial recognition, image tagging, and object detection in various image processing tasks.
3. **Deployment:** Elasticsearch can be deployed on-premises or in the cloud, offering flexibility in the deployment environment. On the contrary, Rekognition API is a cloud-based service offered by Amazon Web Services (AWS), making it suitable for cloud-native applications.
4. **Integration:** Elasticsearch can be easily integrated with various programming languages, databases, and other tools through its robust APIs and connectors. In comparison, Rekognition API offers SDKs for popular programming languages, making it easy to integrate with different applications and services.
5. **Pricing Model:** Elasticsearch is typically open-source with optional paid support plans depending on the deployment method chosen. In contrast, Rekognition API follows a pay-as-you-go pricing model based on the number of images or videos processed, with different pricing tiers for various functionalities.
6. **Customization:** Elasticsearch allows users to customize search queries, indices, mappings, and analyzers to tailor the search results to specific requirements. Conversely, Rekognition API does not provide much customization in terms of algorithm parameters or training models, as it relies on pre-trained machine learning models for image analysis tasks.
In Summary, Elasticsearch is a highly scalable search and analytics engine suitable for indexing and searching large volumes of data, while Rekognition API is a cloud-based image analysis service that excels in object detection, facial recognition, and image tagging tasks.
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 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
Pros of Rekognition API
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of Rekognition API
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