Use MongoDB Atlas as the data store for your solution
MongoDB is a general purpose, document-based, distributed database built for application developers. It provides an integrated suite of products and services, including Atlas Search, that developers can use to build applications faster than ever before.
Example: Data Platform for microservices and Flexible Data Model
Build an AI powered experience using Atlas Vector Search - More info
Atlas Vector Search lets you search unstructured data. You can create vector embeddings with machine learning models like OpenAI and Hugging Face, and store and index them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, dynamic personalization, and other use cases.
Example: Semantic Search, AI Chatbot, AI Recommendation Engine
Provide relevance based app features using Atlas Search - More info
Atlas Text Search allows for fine-grained text indexing and querying of data on your Atlas cluster. It enables advanced search functionality for your applications without any additional management or separate search system alongside your database. Some use cases for Atlas Text Search include enabling users to quickly find what they are looking for on a website, promoting certain products on an e-commerce site, and providing fast, relevant search results for any application.
Example: Advanced Search
Include real-time analytics and data visualization using Atlas Charts - More info
MongoDB provides real-time analytics and data visualization capabilities through its built-in data visualization tool, Atlas Charts. With Atlas Charts, developers can easily create, share, and embed rich dashboards built from their own data in the cloud. Charts provides a wide variety of chart types to visualize data, including bar charts, scatter plots, geospatial charts, and more. Additionally, Charts provides built-in aggregation functionality, allowing developers to process collection data by a variety of metrics and perform calculations such as mean and standard deviation to provide further insight into their data.
Example: Dashboard, Embedded Charts
Use Time Series Collections for storing sequences of measurements over a period of time - More Info
MongoDB Time Series Collections are optimized for the demands of analytical and IoT applications by offering reliable data ingestion, a columnar storage format, and fast query processing. This cost-effective solution is designed to meet the most demanding requirements for performance and scale.
Example: Event analytics