New capabilities allow companies to build new classes of applications across the entire enterprise using a single developer data platform
MongoDB has unveiled a series of new products and features for its developer data platform, MongoDB Atlas. These enhancements aim to empower customers with faster and more efficient ways to create modern applications for various workloads.
The five new capabilities for the data platform include Atlas Vector Search, Atlas Search Nodes, Atlas Stream Processing, scaling and efficiency improvements for Time Series collections, and expanded capabilities using Atlas Data Federation on Microsoft Azure.
MongoDB Atlas is a multi-cloud developer data platform designed to simplify and accelerate application development. It provides a comprehensive set of data and application services within a unified environment, enabling developer teams to rapidly build applications with the necessary capabilities, performance, and scalability.
“The new MongoDB Atlas capabilities announced today are in response to the feedback we get from customers all around the world—they love that their teams are able to quickly build and innovate with MongoDB Atlas and want to be able to do even more with it across the enterprise,” said Dev Ittycheria, President and CEO at MongoDB. “With the new features we’re launching today, we’re further supporting not only customers who are just getting started, but also customers who have the most demanding requirements for functionality, performance, scale, and flexibility so they can unleash the power of software and data to build advanced applications to transform their businesses.”
The key advancements to the data platform include the integration of generative AI capabilities with Atlas Vector Search. This feature enables organizations to build next-generation applications that leverage generative AI for highly personalized end-user experiences and improved productivity. Existing technology stacks often lack the flexibility to store and process different types of data required by AI models like large language models (LLMs), which rely on vector-based data representation. Atlas Vector Search allows customers to power a wide range of workloads, from semantic search to image comparison and personalized recommendations, using a single unified platform. Additionally, it provides the ability to enhance pre-trained AI models with custom data for domain-specific results. MongoDB Atlas Vector Search seamlessly integrates with open-source frameworks like LangChain and LlamaIndex, facilitating access to LLMs from MongoDB partners and model providers.
Another addition to the data platform is Atlas Search Nodes, which offers a dedicated infrastructure for scaling search workloads independently from the database. This feature enables workload isolation, resource optimization, and improved performance at scale. Atlas Search empowers customers to integrate relevance-based search capabilities into their applications effortlessly. With the introduction of this feature, customers gain dedicated resources to scale their search workloads with greater flexibility and control, delivering enhanced relevance-based and AI-powered search experiences to end-users.
The company has introduced Atlas Stream Processing to the data platform to address the increasing demand for processing high-velocity streams of complex data. This feature modernizes data processing by allowing organizations to engage end-users in real-time and optimize business operations as conditions change. Traditional relational data schemas often struggle to handle real-time streaming data effectively. Developing applications that incorporate streaming data often involves complex tooling, resulting in longer development cycles and higher costs. Atlas Stream Processing simplifies the extraction of insights from high-velocity and high-volume streaming data, empowering organizations to build highly engaging applications that analyze data in real-time for personalized experiences and efficient business operations.
Furthermore, the company has enhanced the scalability and flexibility of its Time Series collections to handle enterprise-scale workloads more effectively. Time series workloads, where large volumes of data are processed from devices or sensors, require the ability to modify ingested data. Time Series collections now provide improved scaling capabilities and the option to modify data already ingested, ensuring accuracy in future analyses and enabling flexibility as real-world conditions change. These enhancements enable customers to effectively manage mission-critical time series workloads while meeting strict data governance requirements.
The company has also enhanced the data platform with new multi-cloud options that extend support for Microsoft Azure to Atlas Online Archive and Atlas Data Federation in addition to Amazon Web Services (AWS). Atlas Online Archive allows customers to automatically tier Atlas databases to the most cost-effective cloud object storage option while retaining query capability. The addition of Microsoft Azure support provides customers the flexibility to keep their workloads within a single cloud environment. Atlas Data Federation simplifies the process of reading and writing data between Atlas databases and cloud object stores. With support for Microsoft Azure Blob Storage, customers can seamlessly work with Azure data alongside AWS data, facilitating comprehensive data management across multiple clouds.