MongoDB’s CPO Sahir Azam on data sovereignty and empowering developers
"I've very rarely seen so much industry traction on a big platform shift like this"
For a long time the ability to build powerful AI applications was largely confined to a small circle of advanced, well-resourced teams.
Yet unleashing AI’s potential requires democratised access to tools that empower a much wider pool of developers to build generative AI apps – think of examples like improving customer support, where appalling user journeys are rife, or making it easier to tackle technical debt.
This is increasingly a reality thanks to technology from innovative companies such as MongoDB, whose multi-cloud Atlas database service provides a scalable, secure and highly flexible platform to build on – and intelligent, targeted use of AI itself is making that possible.
At the MongoDB.local London event – one of 29 developer user conferences that MongoDB is hosting around the world this year – AI was centre-stage, as MongoDB announced capabilities that will let everyone from small start-ups to large enterprises, build AI into their applications.
That included a powerful new vector search engine it is calling Atlas Vector Search, which lets users search unstructured data and create vector embeddings with machine learning models like OpenAI and Hugging Face, then store them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, and dynamic personalization.
MongoDB CPO Sahir Azam emphasises fundamentals
Yet speaking in a keynote, Chief Product Officer Sahir Azam took his time to get to AI: MongoDB has been focused on fundamentals too.
That includes delivering significant improvements to working with time series data, especially demanding, high-volume datasets of all shapes with MongoDB 7.0; new algorithms to dynamically adjust the maximum number of concurrent storage engine transactions (including both read and write tickets) to optimise database throughput; delivering encryption and security capabilities that reassure CIOs and CISOs at the most demanding levels, and ensuring its managed database can play quite literally anywhere – including at the most edgy of Edge locations.
Unlocking AI capabilities
Yet AI matters too and MongoDB is innovating hard here and using generative AI itself to further democratise access for developers. A simple example: Converting source data into vectors and enabling developers to easily search based on semantics, such as “give me images that look like…”, for example unlocks vast capabilities without the need to query or transform the data itself, learn a new stack or syntax, or manage a whole new set of infrastructure; reducing friction for developers as they build.
MongoDB CPO: “An AI application still needs resiliency, scalability…”
“It’s easy to be swept up in the hype of AI but, as a customer recently said to me, an AI application is still an application, which means it still needs resiliency, scalability, performance, flexibility and the underlying operational capability,” Sahir Azam, CPO at MongoDB, told The Stack.
“Already we have seen over 500 companies choose MongoDB as a core database for the foundation of their AI applications,” he added.
“What we've done now is extend that capability to support new abilities to store and manage data, such as vectors in our system, which allows you to search for different objects based on their characteristics more seamlessly. We haven’t simply bolted on a separate vector search database like you see in other environments, but rather brought together the flexibility of MongoDB's document model with the capabilities of vector search in a single engine that people are familiar with. This will really make it so much easier to add AI capabilities to an application.
“For me, this capability is very exciting," he adds.
"I've very rarely seen so much industry traction on a big platform shift like this. Fifteen years ago or so there was the whole cloud and public cloud shift. Now AI is top of mind and the ability for us to build technology that makes it easier for the average developer or team to create these really sophisticated applications is very exciting.”
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While trendy AI advancements might dominate the headlines, there were also plenty of other announcements at MongoDB.local London to appeal to the more bread-and-butter needs of the developer community.
Greater speed, for instance, is constantly craved by developers to streamline their tasks, so many cheered the news that MongoDB is now using generative AI technology to enable developers to query data in natural language. When asked questions, simple or complex, in plain English, MongoDB’s GUI (Compass) will automatically generate the corresponding query in MongoDB query language MQL. By reducing friction in the development process and negating the need to learn MongoDB’s query language, this feature will enable all developers to iterate and build faster.
Also popular was the news that SQL query conversion in Relational Migrator can now convert queries and stored procedures to MongoDB query language at scale, seamlessly shifting resources from query creation to review and implementation. Converting queries and application code is one of the most difficult and time-intensive steps when migrating from using a relationship database to NoSQL, so this development was warmly received among developers.
"Always a key investment"
“Requirements among our developer community are always increasing. People are building more sophisticated apps that need to be more secure and serve more and more users with faster and faster performance,” said Azam. “That’s why improving the core MongoDB platform, making it fit for the next generation of applications, is always a key investment for us.
“There are new capabilities that are part of the platform, whether it be vector or relevance-based search or time series, but oftentimes it's hard to unlock these new capabilities because you’re stuck in legacy relational database systems. Our relational migrator helps you modernise your applications off of traditional databases by modernising the data model, migrating the data and, now, automatically converting legacy SQL queries into the MongoDB query language, which removes the barrier to entry and adds to the ease at which organisations can adopt MongoDB.”
