November 2, 2024

 

 

azure nosql

2 db  cosmos db

Couchbase Features and Hybrid Architecture Use Cases

Use Case Handled by Couchbase Alone Requires Hybrid Architecture with GPU
Image Storage and Retrieval – Store images as binary data (e.g., base64-encoded) in Couchbase. <br>- Efficient querying and indexing using N1QL and Full-Text Search (FTS). <br>- Simple retrieval for display purposes. – Not needed.
Metadata Management – Store and retrieve image metadata (e.g., tags, descriptions) using JSON documents. <br>- Perform complex queries on metadata. – Not needed.
Basic Image Processing – Storing pre-processed images. – Real-time image processing tasks like resizing, cropping, and filtering can benefit from GPU acceleration.
Image Classification – Store pre-classified images and their labels. – Training and running deep learning models for image classification require GPU acceleration for faster performance.
Object Detection – Store detected objects’ metadata along with the images. – Training and real-time inference for object detection models require GPUs for efficient processing.
Image Similarity Search – Store feature vectors for images and use Full-Text Search for basic similarity search. <br>- Advanced similarity search can be enabled with vector storage and retrieval. – Advanced similarity search using deep learning models to extract features and compare them requires GPUs.
Image Generation and Enhancement – Store generated or enhanced images. – Generative models (e.g., GANs) for image creation and enhancement need GPU acceleration for training and real-time processing.
Augmented Reality (AR) Applications – Store AR-related data and metadata. – Real-time image processing and overlay rendering for AR applications require GPU capabilities.
Video Frame Extraction – Store individual frames extracted from videos as images. – Real-time video frame extraction and processing require GPUs for efficient performance.
Face Recognition – Store face embeddings and metadata. – Training and real-time inference of face recognition models require GPU acceleration.
Image Compression and Decompression – Store compressed images and metadata about compression. – Real-time compression and decompression tasks benefit from GPU acceleration to handle large volumes of data quickly.
Image Annotation and Labeling – Store annotated images and their labels in JSON documents. – Automated or semi-automated image annotation using deep learning models can benefit from GPU acceleration for efficiency.
Medical Imaging Analysis – Store medical images and related metadata. – Advanced analysis of medical images (e.g., MRI, CT scans) using deep learning models requires GPUs for high performance and accuracy.
Geospatial Image Analysis – Store geospatial images (e.g., satellite images) and metadata. – Processing and analyzing geospatial data using machine learning models for tasks like land cover classification or change detection require GPU acceleration.
Storing Images as Vectors – Store feature vectors of images for AI-driven tasks such as similarity search and classification. – Extracting vectors from images using deep learning models and storing them for further AI applications require GPU acceleration for training and inference tasks.

Summary

  • Binary Storage: Directly stores image data for simple retrieval and display purposes. Ideal for straightforward storage and retrieval tasks.
  • Vector Storage: Stores processed image data as vectors for advanced analysis and AI-driven applications. Requires preprocessing with machine learning models to convert images into feature vectors.

Key Points

  • Binary Data: Suitable for applications where images need to be stored and retrieved without additional processing.
  • Vector Data: Suitable for applications that require image analysis, similarity search, and machine learning functionalities.

 

 

https://chatgpt.com/share/8528e14f-4c63-4cf4-92f3-679ee0fb84dd

 

https://chatgpt.com/share/b02fc5ac-88af-441d-8527-5898bef47052

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