MongoDB and Carahsoft have partnered to provide a series of self-guided tours for MongoDB's enterprise-ready Artificial Intelligence solutions. Similar to a live demonstration, these in-depth walkthroughs explore MongoDB's wide array of use cases that can help meet you and your organization’s unique IT needs.
Learn about MongoDB’s Artificial Intelligence solutions by starting a self-guided tour below or schedule time with your dedicated MongoDB representative for personalized insights.
MongoDB offers a developer data platform that’s built to help improve constituent welfare by maximizing the power of an agency’s data, reduce database spending, and meet the strictest of compliance standards. Ran on an open source document database with a horizontal scale-out architecture and a flexible schema, MongoDB offers innovative and flexible data management, analysis, and app development. As a NoSQL database, MongoDB stores data in BSON (binary JSON) documents instead of tables or rows or columns. This scale-out architecture develops scalable applications with evolving data schemas. Developers can store structured or unstructured data easily using MongoDB’s document database. MongoDB handles high volume and scales both vertically and horizontally to accommodate large data loads, empowering government to go further and faster when developing software applications.
Atlas for Government is one of the most innovative cloud database service on the market. It provides the versatility needed to modernize legacy applications and support the unique requirements and missions of the U.S. government – in a secure, fully-managed, dedicated FedRAMP® authorized environment. A4G is the most secure way for the U.S. government to deploy, run, and scale MongoDB in the cloud.
Create intelligent applications using semantic search and generative AI across diverse datasets. Combine your operational database and vector search seamlessly in one unified platform with a MongoDB native interface, allowing the utilization of large language models (LLMs) through well-known frameworks. Developers can leverage the power of Atlas Vector Search to craft AI-driven experiences, accessing comprehensive data via the MongoDB Query API for a unified and consistent developer experience. The introduction of the $vectorSearch aggregation stage further simplifies the process for existing MongoDB users.
MongoDB's generative AI capabilities empower developers to build intelligent applications with advanced functionalities. Leveraging large language models (LLMs) through popular frameworks, MongoDB enables the creation of sophisticated AI-driven experiences. With MongoDB's generative AI, developers can seamlessly integrate semantic search and access diverse datasets, providing a powerful tool for enhancing application intelligence.
Document databases utilize the intuitive, flexible document data model to store data. Document databases are general-purpose databases that can be used for a variety of use cases across industries. MongoDB's flexible schema allows for the dynamic and agile structuring of data, accommodating changes without requiring a predefined schema. This flexibility is particularly beneficial in scenarios where the data structure evolves over time.
Transform building event-driven applications by continuously processing streams of data with a familiar developer experience. Atlas Stream Processing integrates the document model, adaptable schemas, and a robust aggregation language, offering an elevated level of capability and ease for developing applications that require processing complex event data at scale. Atlas Stream Processing paves the path to a responsive and reactive, real-time organization. Capabilities include continuous processing, continuous validation and continuous merge.
Build and run data-intensive analytical applications by combining the flexibility of the document model with time series collections. Simplify and accelerate application development with native time series collections that automatically handle the complexities and challenges of time series data, without the need for extra instrumentation by developers. In addition, columnar storage and compression optimize for query speed and cost efficiency, even as data grows over time.