MongoDB
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Even if you’ve never heard of MongoDB, you’ve likely come across it in your daily online life. MongoDB has enabled over 43,000 organizations, including leading companies in technology, healthcare, telecommunications and financial services, to build solutions leveraging his MongoDB technology. The company’s horizontal document-oriented (often called NoSQL) database technology supports a wide range of workloads that require modern data services. These needs often do not correspond directly to the constraints of traditional relational databases.
Rapid innovation and rapid product cycles are required to meet the rapidly evolving needs of modern application development. MongoDB demoed both last week at his MongoDB.local 2023 event in New York City, showcasing an exciting set of new features and services.
The announcement announces new capabilities that leverage the latest AI technologies, increases developer productivity, features that can ease the burden of enterprise application development, and introduces MongoDB technology to a range of targeted verticals. It covers a wide range of areas, including new programs that simplify. . There’s a lot to dig into.
Realization of AI
Today, it is impossible to talk about application development without mentioning artificial intelligence. Generative AI, represented by Large Language Models (LLMs) such as ChapGPT, captures everyday headlines. The question that tech companies and his IT practitioners alike ask me most often is how AI will impact businesses. Last week MongoDB showed how generative AI impacts the data plane.
MongDB Atlas Vector Search
Technologies such as generative AI change the way we think about managing the data that feeds AI-driven systems. For example, language processing makes use of attributes of data called “vectors.”
Vector embeddings can be thought of as tags placed in the data as AI models that define relationships between words. These vectors are used as efficient shortcuts when running generative AI models (this is a simplistic description of vectors, interested readers should read this more detailed description).
MongoDB’s new MongoDB Atlas Vector Search is designed to simplify the development of AI languages and generative AI applications. This new feature makes it possible to embed vectors directly into data stored in MongoDB, allowing new generative AI applications to be developed quickly and efficiently on his MongoDB Atlas.
MongoDB Vector Search
MongoDB
MongoDB Atlas Vector Search is also integrated with the open source LangChain and LlamaIndex frameworks with tools to access and manage LLMs for various applications.
MongoDB AI Innovator Program
Building and deploying applications that leverage the latest AI technologies can be challenging. The concepts, tools, and even infrastructure are very different from traditional software development approaches. AI applications can require multiple iterations of model training as the application evolves, which can significantly increase development costs.
Last week, MongoDB recognized the unique challenges of AI application development and announced a new MongoDB AI Innovators Program aimed at easing the unique burden of AI application development. The new program offers several benefits, including offering eligible organizations up to $25,000 in MongoDB Atlas credits.
The AI Innovators program also includes opportunities to work with MongoDB, in what the company calls the AI Amplify track, to rapidly advance strategic partnerships and joint go-to-market activities. Companies participating in the AI Amplify track will have their submissions evaluated by MongoDB to determine suitability for potential partnerships. MongoDB technical experts also address solution architectures and help identify compelling use cases for co-marketing opportunities.
Finally, MongoDB makes its partner ecosystem available to program participants. Organizations participating in the MongoDB AI Innovators Program will now receive priority access to opportunities with MongoDB partners, and eligible organizations will quickly join his MongoDB partner ecosystem for seamless, interoperable build powerful integrations and joint solutions. At MongoDB, he has over 1,000 partners, which is a compelling advantage of the program.
New MongoDB Atlas features
In addition to the new vector search capabilities already mentioned, MongoDB Atlas introduces four additional features.
- MongoDB Atlas Search Node now provides dedicated infrastructure for search use cases. This allows customers to scale independently of their databases to manage unpredictable spikes and high-throughput workloads with greater flexibility and operational efficiency.
- MongoDB Atlas Stream Processing transforms building event-driven applications that react and respond in real time by unifying how developer teams work with data in motion and data at rest.
- The MongoDB Atlas Time Series collection makes time series workloads more efficient for use cases at scale, from predictive maintenance of factory equipment to automotive fleet monitoring to financial trading platforms.
- With new multi-cloud options for MongoDB Atlas Online Archive and Atlas Data Federation, customers can now seamlessly tier and query their data in Microsoft Azure in addition to Amazon Web Services.
Keeping with the theme of simplifying the developer experience, these new features ease the burden of developing applications that use MongoDB Atlas as an intelligent data platform.
Lighten the burden on developers
MongoDB is a foundational component of data modernization, but only part of the solution. Mongo recognizes this and calls their technology a “developer data platform.” This phrase emphasizes the importance of allowing developers to use his AI while building the next generation of his AI-enabled applications. MongoDB empowers developers by providing a data plane that provides the functionality most needed by modern applications.
Mongo announced support for new programming languages to facilitate adoption in multiple environments. The company added support for server-side Kotlin applications (Kotlin is a programming language designed for cross-platform application development). MongoDB also adds new support for data processing and analytics using Python with the general availability of the open source PyMongoArrow library, allowing developers to use some of the most popular Python-based analytics frameworks. Enables efficient transformation of data stored in MongoDB.
MongoDB also adds support for deploying and managing MongoDB using the Infrastructure as Code (IaC) capabilities of Amazon AWS. MongoDB has released a new integration with the AWS Cloud Development Kit (CDK). This will allow the developer to manage his MongoDB Atlas resources using his C#, Go, Java and Python. This is a key enabler for developers deploying to AWS.
MongoDB has also simplified Kubernetes integration with improvements to the MongoDB Atlas Kubernetes Operator. This new feature enables developers to install MongoDB’s horizontal document-oriented (often referred to as NoSQL) database technology for a wide range of workloads that require modern data services, i.e. the constraints of traditional relational databases. It can support needs that are often not addressed directly.
Finally, MongoDB announced a new MongoDB Relational Migrator tool. New tools make migrating traditional legacy databases to MongoDB environments easier and significantly faster. MongoDB Relational Migrator analyzes legacy databases, automatically generates new data schemas and code, and performs seamless migrations to MongoDB Atlas without downtime. This feature reduces the pain commonly experienced when moving data from traditional data stores to new environments.
Analyst view
MongoDB held an investor conference alongside its developer-focused MongoDB.local event. At the investor event, MongoDB Chief Product Officer Sahir Azam explained how the company builds its product strategy and his GTM activities on an understanding of customer journeys.
The features and new business opportunities announced by MongoDB are understandable to anyone accustomed to developing modern data-driven applications. The new product will help developers leverage his MongoDB technology to create new applications while also implementing the features needed to develop the next generation of his AI-enabled solutions.
There is no doubt that developers appreciate what the company has to offer. As an enabling technology for other applications, MongoDB’s approach not only makes sense, it’s necessary. It’s paid off, too.
MongoDB beat consensus forecasts for 17th consecutive quarter, with the latest earnings beating EPS estimates by nearly 195%. In the most recent quarter, Mongo sales were up 29% year-over-year. The company has grown its revenue eightfold since 2018. It has earned tremendous customer trust, especially in a market that still hinders the growth of nearly all underlying technology companies.
MongoDB competes in a crowded space, and recent announcements from competitors such as Elastic show that innovation is coming from their closest competitors. At the same time, MongoDB is constantly focusing on implementing programs that improve the developer experience, quickly adapt to new trends in data analytics and AI, and enable customers to launch new applications quickly. Stand out in a fiercely competitive environment. 17 consecutive years of revenue growth, over 1,000 partners, and over 43,000 customers all show that MongoDB is a success.
Disclosure: Steve McDowell is an industry analyst and NAND Research is an industry analyst firm providing research, analysis and advisory services to many technology companies (which may include companies mentioned in this article). has been or has been engaged in McDowell has no equity positions in any of the companies mentioned in this article.
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