How to Build Advanced GraphQL-based APIs With MongoDB Atlas and AWS AppSync Merged APIs

Igor Alekseev (AWS) and Babu Srinivasan
June 20, 2023 | Updated: September 9, 2024
>> Announcement: Some features mentioned below will be deprecated on Sep. 30, 2025. Learn more.

When businesses develop their own IT systems, sooner or later, the complexity of managing APIs becomes a challenge. Breaking down monolithic architectures into multiple microservices often results in a proliferation of APIs associated with each microservice. Each API, in turn, has versioning, leading to further fragmentation of the APIs and driving up maintenance costs. If your microservices diagram looks like a hairball you know you are living in API Hell. Conway’s Law, which states that systems mirror the communication structure of the organization, also applies to API development. Different teams build separate and, sometimes, overlapping APIs, further contributing to the fragmentation.

While this is especially true for REST-based APIs, it's also a challenge for GraphQL-based APIs. GraphQL has emerged as a powerful tool for building flexible and efficient APIs that empower developers and elevate user experiences. AWS AppSync is the go-to service for customers looking to accelerate application development with serverless GraphQL and Pub/Sub APIs.

AWS AppSync offers a managed GraphQL service with additional features and capabilities. It simplifies the development of scalable, real-time applications by seamlessly integrating with various data sources, providing offline support, enabling fine-grained authorization and security, and automating infrastructure management. By embracing AppSync, you can harness the full potential of GraphQL while leveraging the benefits of a comprehensive portfolio of services and products provided by AWS.

MongoDB Atlas on Amazon Web Services (AWS) and AWS AppSync combined help developers build scalable, secure, and serverless applications. By seamlessly integrating MongoDB as a data source within AppSync, you're able to leverage MongoDB's flexible document model and AppSync's GraphQL-based querying to efficiently retrieve and manipulate data. And you can leverage AppSync's automatic scaling, ensuring optimal performance. This combined solution enables you to build high-performing serverless applications while simplifying application development.

AWS AppSync recently added a feature called Merged APIs that allows you to compose multiple GraphQL source APIs into a single GraphQL API. Merged APIs give developers the ability to compose distinct APIs developed by different teams into a single, combined GraphQL schema. The merged API resolver function contains the logic to consolidate the source details. The resulting single GraphQL API can be cached for better performance. You can then present the unified API to the clients as a single API endpoint.

AppSync Merged APIs combine MongoDB Atlas-backed APIs with other APIs, allowing you to enrich operational data residing in MongoDB Atlas with data coming from additional sources. You can serve data with a unified GraphQL schema across multiple data sources, including MongoDB.

If you're interested in learning more about this powerful integration, check out our new tutorial that demonstrates two ways to combine MongoDB Atlas with AWS AppSync: leveraging the Drivers and the Atlas Data API. Both approaches work with the AWS AppSync Merged API as well.

Checkout our tutorial on GitHub.

Try out MongoDB Atlas on AWS and AWS AppSync today.

Sign up for MongoDB Atlas on AWS Marketplace Today

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Fueling Pricing Strategies with MongoDB and Databricks

Deploying real-time analytics solutions with the right tech stack can have transformative benefits. Retailers want to grow their brand name or improve customer experience with value based pricing, whilst remaining competitive and cost effective. Despite their ambition to become “data driven” operations, companies often fail in their endeavors, at the core of this is the struggle to do real-time analytics. We will explore the architecture in Figure 1 and discuss the advantages of integrating MongoDB Atlas and Databricks as a perfect pairing for retail pricing strategies using real time AI. The solution we’ll describe integrates concepts from Event Driven architecture in the data generation and ingestion side, real time analytics orchestration, machine learning and microservices. Let’s get started! Figure 1:  Overview of a dynamic pricing solution architecture Reduce friction with flexibility The pricing data complexity for a retailer with a diverse product line increases due to factors like seasonal sales, global expansion, and new product introductions. Tracking and analyzing historical prices and product offerings become more challenging as they change throughout the year. Analytics solutions built around event driven architectures try to explain what is happening in a specific system or solution based on any significant occurrence such as user actions, data updates, system alerts, or sensor readings. Deciding which occurrences are crucial to understand your customers and instrument your business model around that is when things start to become more intricate. Specially when trying to instrument your data models using traditional relational database management systems and its disadvantage when it comes to pairing that data structure with object oriented applications. The inability of a retailer to adapt it’s data model to the customer behavior quickly translates into friction and a weaker presence in the market, for example poor pricing strategies compared to competitors because they lack information of historic price points and how they vary between products. Figure 2:  An inflexible data model is a road block for innovation That friction is contagious throughout the whole value chain of an organization, affecting the semantic layer of business (a bridge between the technical data structures and business user understanding), generating data inconsistencies, and reducing time to insight. The capacity of your conceptual data model to adapt to an ever changing customer behavior helps reduce that friction significantly as its flexibility allows for a more intuitive data modeling of real world events. For your real time pricing strategies challenges, MongoDB Atlas document model with its embedding and extended reference capabilities becomes the perfect tool for the job, as it allows for faster feature development and stronger test driven growth and talent retention as a consequence. In combination with it’s high performance queries and horizontal scalability the solution becomes robust as it will handle the high throughput of clickstreams on your ecommerce applications and yet be able to respond real time data driven decision making features. Its ease of integration with other platforms thanks to strong API capabilities and drivers make it the perfect solution on top of which to build your business operational and intelligence layers as you’ll avoid vendor lock-in and data scientists can easily leverage AI frameworks to work with fresh data. It’s distributed by default principle, plus following best practices principles guarantee your operational data layer will handle the workload needed. As the AI algorithms analyze vast amounts of historical and real-time data to make pricing decisions, having a distributed platform like MongoDB enables efficient storage, processing, and retrieval of data across multiple nodes. From what? To why? The intelligence layer To unlock meaningful market growth and achieve it at scale your analytics need to evolve from just understanding what is happening by querying and analyzing historical data, to understand why the events measured with your operational data layer are happening and even further try to forecast them. For a pricing solution, retailers would need to gather historical pricing points data for their product lines and shape through ETL (Earn, Transform, Load) pipelines to feed machine learning algorithms. This process is often complicated and brittle using the traditional data warehousing approach, often incurring in data duplication making it difficult and costly to manage. Figure 3:  Reduced friction thanks to seamless integration of the different data layers The advantage of using MongoDB Atlas as your operational data layer solution, is that through its Aggregation Pipelines you can shape your data in any way you need and then through MongoDB App Services , you can instrument Triggers and Functions to simplify this process and then consume that data in Databricks by leveraging the MongoDB Atlas via Spark connector . Databricks provides you with a streamlined way of working with your models, by writing python code on hosted clusters notebooks. You can leverage its MLFlow integration to be able to register experiments which then can be turned into deployed models over an endpoint. So transforming your data and integrating your operational layer, through connectors and API calls as triggers and functions, with your intelligence layer for machine learning and AI, you can easily build a pricing solution that will be able to generate market growth for your organization from its core through a semantic layer acting as a bridge between the technical aspects of data storage and the business requirements of data analysis. Uncover new growth opportunities Designing a real time analytics solution with MongoDB Atlas and Databricks is not only the fastest way to unlock your team's capabilities to devise pricing strategies, it also sets the cornerstone to build automated rules for more complex solutions. Other ways of automating your retail application with AI driven insight could include: optimizing your marketing mix budget by each product price elasticity, adding another analytical layer of customer segmentation data to achieve targeted dynamic pricing, or optimizing your supply chain with sales forecasting in real time. By taking advantage of MongoDB Charts or the MongoDB BI Connector , you can fuel your business dashboards, making that semantic layer of the business model the central point for your teams alignment. Foundations for growth Modern ecommerce sites unleash the power of real time analytics and automation to create better experiences for customers and a more profound approach to customer analytics by unblocking the power of machine learning to discover trends in behavioral data, effectively turning companies into automated growth machines. If you want to learn how to build a dynamic pricing solution integrating MongoDB Atlas and Databricks see our step-by step How To Guide , with all necessary code available on GitHub .

June 20, 2023
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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024