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The MongoDB.local NYC event kicked off last week with a keynote from Dev Ittycheria, CEO of MongoDB. The overarching theme was “Love your developers,” Ittycheria had lots of news to share about enhancements to MongoDB Atlas, its multi-cloud developer data platform with a fully managed non-relational cloud database.

Read on as I highlight the new capabilities for MongoDB Atlas announced at the event.

Build next-generation applications that use generative AI

Searching for something when unsure of its name can be challenging. Vector search solves this problem by allowing you to search by what you mean, providing answers to queries based on context. Under the hood, vectorization converts words into numbers using machine learning (ML) to encode meaning that can then be processed mathematically. Vectors automate synonyms, cluster documents, detect specific meanings and intents in queries and rank results.

MongoDB Atlas Vector Search enables developers to build next-generation applications using generative AI to enhance the end-user experience and improve team productivity. Incorporating technology based on generative AI can be challenging to integrate into applications because it requires storing and processing different data types. For example, large language models (LLMs) require data in the form of vectors, which need specialized databases, resulting in more complexity. MongoDB Atlas uses a flexible and scalable document-based data model that supports data of virtually any type.

MongoDB Atlas Vector Search uses open-source LangChain and LlamaIndex frameworks with tools for accessing and managing LLMs for various applications. The frameworks can access LLMs from MongoDB partners such as AWS, Databricks, Google Cloud, Microsoft Azure and MindsDB and model providers such as Anthropic, Hugging Face and OpenAI.

Easily incorporate AI into applications

Developers can now use MongoDB Atlas Vector Search with Google Cloud’s Vertex AI LLMs. Vertex AI provides the API to generate embeddings from customer data stored in MongoDB Atlas, combined with the PaLM text models to perform semantic search, classification, outlier detection, AI-powered chatbots and text summarization.

MongoDB and Google Cloud professional services teams can help to prototype applications by providing expertise on data schema and indexing design, query structuring and fine-tuning AI models. When ready for production, the MongoDB and Google Cloud professional services teams can optimize applications and help solve future problems through quick iteration to get new features into production more quickly.

All in all, MongoDB and Google Cloud provide a growing set of solutions and integrations to enable developers to build applications that take advantage of new AI technologies quickly.

Extract insights from high-velocity, high-volume streaming data

Streaming data is rich, heterogeneous and constantly changing—requiring a flexible and scalable data model that can quickly evolve as conditions change.

Real-time streaming data from IoT devices, end-user browsing behaviors and inventory feeds is critical to modern applications because it provides the ability to engage end users with real-time experiences as behaviors change and to optimize business operations as conditions change. Incorporating streaming data into applications today can be complex, especially considering all the specialized programming languages and libraries that often come into play.

MongoDB Atlas Stream Processing is a single interface to quickly extract insights from high-velocity and high-volume streaming data. The company believes it will transform how organizations process streaming data to engage end users and speed up operations. MongoDB Atlas Stream Processing has a flexible data model that works with any data type.

Access to best practices developed through industry experience

Every industry has its own unique set of challenges and needs. But most companies across industries need to urgently modernize and take advantage of the opportunity presented by next-generation applications, data analytics and generative AI. When a provider can offer technical expertise and experience relevant to a specific industry, it can be invaluable to companies trying to get started. MongoDB Atlas for Industries is a new program to help companies accelerate cloud adoption and modernization by leveraging industry-specific expertise, programs, partnerships and integrated solutions. MongoDB Atlas for Industries provides access to experts from both MongoDB and its partners who can discuss and help implement client-specific solutions using best practices developed through proven industry experience

MongoDB Atlas for Industries is launching its first set of vertical solutions for financial services. With MongoDB Atlas, financial institutions can improve customer experiences by modernizing legacy infrastructure, such as in-house banking systems and building composable architectures to get ideas to market faster with high performance and scale. MongoDB Atlas for Industries programs for manufacturing, automotive, insurance, healthcare, retail and other industries will follow later this year.

Wrapping up

I predicted the multi-cloud before it was cool, so it’s no surprise that I am a big fan of what MongoDB is doing with Atlas. I see MongoDB as one of the core companies enabling the hybrid multi-cloud.

My biggest takeaway from the event was the emphasis on growth. It was a big seminal moment for MongoDB in the advancement of its product portfolio, adding new products like vector search and stream processing. MongoDB is looking to grow by integrating generative AI and enterprise search, putting pressure on other point solutions. MongoDB also made the first move to increase market share in vertical markets with Atlas for Industries, starting with Financial Services.

Another crucial feature of MongoDB Atlas is its freedom to run across AWS, Azure or Google Cloud. As customers run workloads on different clouds, MongoDB Atlas allows data to be distributed in a single cluster across multiple public clouds simultaneously and move workloads seamlessly between them. MongoDB Atlas can future-proof applications with the option of moving from cloud to cloud as needed—without costly data migration.

Finally, the emphasis on simplification was powerful, offering a single API and a document model for various data needs, with even a SQL migrator tool to help companies migrate from other SQL providers to MongoDB.

Overall, it was a great event, with MongoDB making all the right moves to gain market share.

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