Announcing the GA of the Atlas Device SDK for C++

Otso Virtanen, Lee Maguire, Jason Flax, and Yavor Georgiev
February 22, 2024 | Updated: April 24, 2024

This post is also available in: Deutsch, Français, Español, Português, Italiano, 한국어, 简体中文.

MongoDB's developer data platform was designed to offer unparalleled flexibility and scalability for developers. By streamlining the integration of complex data structures and real-time analytics, and accelerating the development and deployment of mission-critical applications, its adoption has added significant value to businesses across industries. Today we continue our mission to provide the best experience for developers and are excited to announce the general availability (GA) of the Atlas Device SDK for C++. The updates in this release come after numerous iterations that were guided by feedback from our preview users and target performance and portability.

The Atlas Device SDK for C++ enables developers to effortlessly store data on devices for offline access while seamlessly synchronizing data to and from the MongoDB Atlas cloud within their C++ applications. It serves as a user-friendly alternative to SQLite, offering simplicity due to its object-oriented database nature, removing the necessity for a separate mapping layer or ORM. Aligned with MongoDB's developer data platform mission of streamlining the development process - the C++ SDK incorporates networking retry logic and advanced conflict merging functionality, eliminating the traditional need for writing and maintaining extensive and complex synchronization code.

Why choose the Atlas Device SDK for C++?

The Atlas Device SDK for C++ is particularly well-suited for applications in embedded devices, IoT, and cross-platform scenarios. It serves as a comprehensive object-oriented persistence layer for edge, mobile, and embedded devices, offering built-in support for synchronization with the MongoDB Atlas as a cloud backend. In the evolving landscape of connected and smart devices, the demand for more data, including historical data for automated decision-making, highlights the importance of efficient persistence layers and real-time cloud-syncing technologies which are robust towards changing network connections and outages.

The database included in the Atlas Device SDK for C++, comes with over a decade of history, and is a mature, feature-rich, and enterprise-ready technology, integrated into tens of thousands of applications on Google Play and the Apple App Store with billions of downloads. Its lightweight design is optimized for resource-constrained environments. It considers factors like compute, memory, bandwidth, and battery usage in its design. Embedding the SDK directly into application code eliminates the need for additional deployment tasks and simplifies the development process.

The fully object-oriented nature of the SDK guides the data modeling, providing a straightforward and idiomatic approach. This stands in contrast to alternative technologies like SQLite database, which require an object-relational mapping library, adding complexity and making future development, maintenance, and debugging more challenging. Furthermore, the SDK’s underlying data store enables seamless integration with reactive UI layers across various environments. In the Atlas Device SDK for C++ we give examples of how to integrate with the Qt framework, but other UI layers can also be added.

Improvements in the GA release

The new API was developed based on performance measurements with a coordinated focus and effort to improve the read/write operations of the data layer. There has been great interest from major automotive and manufacturing OEMs and this feedback has been invaluable in guiding our final API.

Some of the changes added to the Atlas Device SDK for C++ include:

Aligning our APIs with other Atlas Device SDKs, e.g. improved control of the database state with monitoring and manual compaction
HTTP tunneling
Better control for the Atlas Device Sync sessions
Windows support
Compatibility with OpenWRT among other Linux distributions by supporting musl
Android Automotive support with Blueprint/Soong build files

What's next

Looking ahead we are working towards geospatial support as well as the ability to build with a variety of package managers such as vcpkg and Conan. We welcome and value all feedback - if you have any comments or suggestions, please share them via our GitHub project.

Ready to get started?

Install the Atlas Device SDK for C++ — start your journey with our docs or jump right into example projects with source code.

Then, register for Atlas to connect to Atlas Device Sync, a fully managed mobile backend as a service. Leverage out-of-the-box infrastructure, data synchronization capabilities, network handling, and much more to quickly launch enterprise-grade mobile apps.

Finally, let us know what you think, and get involved in our forums. See you there!

← Previous

Should I Begin a Pre-Sales Career at MongoDB? Insights from Our Remote Solutions Center

Do you have a technical background and enjoy working with customers? Have you considered beginning a career in pre-sales? Aicha Sarr, Solutions Architect at MongoDB, shares insights into how our Remote Solutions Center offers a path to building a career in pre-sales. Read on to learn more about how our Remote Solutions Center team builds off of each other’s strengths, applies their technical expertise, and focuses on customer success. Diverse backgrounds, shared attributes Our Remote Solutions Center team values both diverse backgrounds and sharing common attributes. Team members possess a blend of technical expertise and a customer-focused mindset with a strong affinity for technology and an inherent curiosity to grasp knowledge related to MongoDB, databases, and complex technical concepts. We all come from a variety of backgrounds, such as data engineers, software developers, cloud architects, and sales engineers. While direct experience with MongoDB is beneficial, it's not mandatory. Similarly, a background as an engineer can be advantageous, but it's not strictly necessary. To succeed on the team, one needs a keen interest in the technology and architecture of systems, an appetite for learning, and a commitment to ensuring customer success. We are trusted advisors, collaborating closely with customers to design and implement reliable systems, making a strong technical alignment between the platform and customer needs that are pivotal for success. Hands-on application of technical skills I’m frequently engaged in projects that require me to apply my technical skills. Many of these projects have been focused on building reusable demos that are distributed to our global team, providing an opportunity for my work to be showcased during technical calls or workshops. For instance, a teammate and I developed a mobile application centered around MongoDB Atlas for the Edge , our edge computing solution. The project included demonstrating the synchronization of user data between intermittently internet-connected devices and a central database, MongoDB Atlas. This demo showcased the practical application of Atlas Edge computing, maintaining data consistency across various devices, reflecting the real-world implications of mobile application development and data synchronization challenges. Additionally, I’ve developed an application illustrating the implementation of a Client-Side Field Level Encryption (CSFLE)-enabled application using Amazon Web Services Key Management Service with Java Spring Boot as the programming language. This hands-on experience allowed me to showcase the robust security features of CSFLE, a feature that enables the encryption of data in an application before it is sent over the network to MongoDB - a tangible example of how to enhance application security within a cloud environment. In my customer calls, I’ve used both my demos and demos built by my colleagues. Hence, these hands-on experiences play a crucial role in enhancing our team's collective knowledge, capabilities, and resources. This collaboration between team members fosters a shared understanding and serves as a valuable learning opportunity, allowing us to disseminate the acquired knowledge internally and promote a culture of continuous learning and skill development. The customer engagement journey Our customer engagement journey begins with establishing strong relationships during initial contact and holding in-depth discussions to grasp the customer's business requirements. This phase serves as the foundation for crafting tailored proposals that outline effective solutions. Technical presentations elucidate proposed solutions' intricacies, involving iterative, collaborative discussions and adjustments based on continuous feedback from customer stakeholders, including IT teams and decision-makers. Transitioning to the implementation phase, Proof of Concept projects may be initiated for real-world solution applications. The seamless move to implementation teams ensures ongoing support, addressing post-implementation issues for continuous customer satisfaction. This holistic approach emphasizes effective communication, active listening, and building lasting partnerships grounded in trust. Engaging with diverse customer personas, each playing a distinct role in decision-making, involves tailored interactions. Technical decision-makers engage in in-depth technical discussions, addressing integration concerns and compatibility. Business decision-makers require presentations emphasizing business value, ROI, and alignment with overarching goals. Interactions with developers or data engineers involve technical implementation discussions and collaborative problem-solving in specific programming languages. These interactions with end users, project managers, and technical teams are essential for gathering insights into practical requirements, project timelines, and seamless implementation. The shared goal is to create solutions that meet business and technical requirements, ensuring feasibility, efficiency, and alignment with customer capabilities. Conclusion: A tapestry of skills and collaboration The success of our Remote Solutions Center lies in the tapestry of diverse backgrounds, shared attributes, and a commitment to continuous learning. The opportunities for hands-on application of technical skills not only strengthen our individual capabilities but also contribute to a collective knowledge pool within our team. The customer engagement process is a collaborative journey, with an emphasis on the importance of understanding and adapting to evolving customer needs. This holistic approach to team dynamics, skill development, and customer engagement positions our Remote Solutions Center for success in a rapidly changing technological landscape. Learn more about careers in solutions consulting at MongoDB.

February 21, 2024

Next →

Better Digital Banking Experiences with AI and MongoDB

Interactive banking represents a new era in financial services where customers engage with digital platforms that anticipate, understand, and meet their needs in real-time. This approach encompasses AI-driven technologies such as chatbots, virtual assistants, and predictive analytics that allow banks to enhance digital self-service while delivering personalized, context-aware interactions. According to Accenture’s 2023 consumer banking study , 44% of consumers aged 18-44 reported difficulty accessing human support when needed, underscoring the demand for more responsive digital solutions that help bridge this gap between customers and financial services. Generative AI technologies like chatbots and virtual assistants can fill this need by instantly addressing inquiries, providing tailored financial advice, and anticipating future needs. This shift has tremendous growth potential; the global chatbot market is expected to grow at a CAGR of 23.3% from 2023 to 2030 , with the financial sector experiencing the fastest growth rate of 24.0%. This shift is more than just a convenience; it aims to create a smarter, more engaging, and intuitive banking journey for every user. Simplifying self-service banking with AI Navigating daily banking activities like transfers, payments, and withdrawals can often raise immediate questions for customers: “Can I overdraft my account?” “What will the penalties be?” or “How can I avoid these fees?” While the answers usually lie within the bank’s terms and conditions, these documents are often dense, complex, and overwhelming for the average user. At the same time, customers value their independence and want to handle their banking needs through self-service channels, but wading through extensive fine print isn't what they signed up for. By integrating AI-driven advisors into the digital banking experience, banks can provide a seamless, in-app solution that delivers instant, relevant answers. This removes the need for customers to leave the app to sift through pages of bank documentation in search of answers, or worse, endure the inconvenience of calling customer service. The result is a smoother and user-friendly interaction, where customers feel supported in their self-service journey, free from the frustration of navigating traditional, cumbersome information sources. The entire experience remains within the application, enhancing convenience and efficiency. Solution overview This AI-driven solution enhances the self-service experience in digital banking by applying Retrieval-Augmented Generation (RAG) principles, which combine the power of generative AI with reliable information retrieval, ensuring that the chatbot provides accurate, contextually relevant responses. The approach begins by processing dense, text-heavy documents, like terms and conditions, often the source of customer inquiries. These documents are divided into smaller, manageable chunks vectorized to create searchable data representations. Storing these vectorized chunks in MongoDB Atlas allows for efficient querying using MongoDB Atlas Vector Search , making it possible to instantly retrieve relevant information based on the customer’s question. Figure 1: Detailed solution architecture When a customer inputs a question in the banking app, the system quickly identifies and retrieves the most relevant chunks using semantic search. The AI then uses this information to generate clear, contextually relevant answers within the app, enabling a smooth, frustration-free experience without requiring customers to sift through dense documents or contact support. Figure 2: Leafy Bank mock-up chatbot in action How MongoDB supports AI-driven banking solutions MongoDB offers unique capabilities that empower financial institutions to build and scale AI-driven applications. Unified data model for flexibility: MongoDB’s flexible document model unifies structured and unstructured data, creating a consistent dataset that enhances the AI’s ability to understand and respond to complex queries. This model enables financial institutions to store and manage customer data, transaction history, and document content within a single system, streamlining interactions and making AI responses more contextually relevant. Vector search for enhanced querying: MongoDB Atlas Vector Search makes it easy to perform semantic searches on vectorized document chunks, quickly retrieving the most relevant information to answer user questions. This capability allows the AI to find precise answers within dense documents, enhancing the self-service experience for customers. Scalable integration with AI models: MongoDB is designed to work seamlessly with leading AI frameworks, allowing banks to integrate and scale AI applications quickly and efficiently. By aligning MongoDB Atlas with cloud-based LLM providers, banks can use the best tools available to interpret and respond to customer queries accurately, meeting demand with responsive, real-time answers. High performance and cost efficiency: MongoDB’s multi-cloud, developer-friendly platform allows financial institutions to innovate without costly infrastructure changes. It’s built to scale as data and AI needs to grow, ensuring banks can continually improve the customer experience with minimal disruptions. MongoDB’s built-in scalability allows banks to expand their AI capabilities effortlessly, offering a future-proof foundation for digital banking. Building future-proof applications Implementing generative AI presents several advantages, not only for end-users of the interactive banking applications but also for financial institutions: Enhanced user experience encourages customer satisfaction, ensures retention, boosts reputation, and reduces customer turnover while unlocking new opportunities for cross-selling and up-selling to increase revenue, drive growth and elevate customer value. Moreover, adopting AI-driven initiatives prepares the groundwork for businesses to develop innovative, creative, and future-proof applications to address customer needs and upgrade business applications with features that are shaping the industry and will continue to do so, here are some examples: Summarize and categorize transactional information by powering applications with MongoDB’s Real-Time Analytics . Understand and find trends based on customer behavior that could positively impact and leverage fraud prevention , anti-money laundering (AML) , and credit card application (just to mention a few). Offering investing, budgeting, and loan assessments through AI-powered conversational banking experience. In today’s data-driven world, companies face increasing pressure to stay ahead of rapid technological advancements and ever-evolving customer demands. Now more than ever, businesses must deliver intuitive, robust, and high-performing services through their applications to remain competitive and meet user expectations. Luckily, MongoDB provides businesses with comprehensive reference architectures for building generative AI applications, an end-to-end technology stack that includes integrations with leading technology providers, professional services, and a coordinated support system through the MongoDB AI Applications Program (MAAP) . By building AI-enriched applications with the leading multi-cloud developer data platform, companies can leverage low-cost, efficient solutions through MongoDB’s flexible and scalable document model which empowers businesses to unify real-time, operational, unstructured, and AI-related data, extending and customizing their applications to seize upcoming technological opportunities. Check out these additional resources to get started on your AI journey with MongoDB: How Leading Industries are Transforming with AI and MongoDB Atlas - E-book Our Solutions Library is where you can learn about different use cases for gen AI and other interesting topics that are applied to financial services and many other industries.

November 26, 2024