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

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 (Atlas GraphQL) 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 discover how to build a simple dynamic pricing solution integrating MongoDB Atlas and Databricks make sure to read this guide.

June 20, 2023
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Unmasking Deception: Harnessing the Power of MongoDB Atlas and Amazon SageMaker Canvas for Fraud Detection

Financial services organizations face growing risks from cybercriminals. High-profile hacks and fraudulent transactions undermine faith in the industry. As technology evolves, so do the techniques employed by these perpetrators, making the battle against fraud a perpetual challenge. Existing fraud detection systems often grapple with a critical limitation: relying on stale data. In a fast-paced and ever-evolving landscape, relying solely on historical information is akin to driving by looking into the rearview mirror. Cybercriminals continuously adapt their tactics, forcing financial institutions to stay one step ahead. The newest tactics often can be seen in the data. That's where the power of operational data comes into play. By harnessing real-time data, fraud detection models can be trained on the most accurate and relevant clues available. MongoDB Atlas, a highly scalable and flexible developer data platform, coupled with Amazon SageMaker Canvas, an advanced machine learning tool, presents a groundbreaking opportunity to revolutionize fraud detection. By leveraging operational data, this synergy holds the key to proactively identifying and combating fraudulent activities, enabling financial institutions to safeguard their systems and protect their customers in an increasingly treacherous digital landscape. MongoDB Atlas MongoDB Atlas , the developer data platform is an integrated suite of data services centered around a cloud database designed to accelerate and simplify how developers build with data. MongoDB Atlas's document-oriented architecture is a game-changer for financial services organizations. Its ability to handle massive amounts of data in a flexible schema empowers financial institutions to effortlessly capture, store, and process high-volume transactional data in real-time. This means that every transaction, every interaction, and every piece of operational data can be seamlessly integrated into the fraud detection pipeline, ensuring that the models are continuously trained on the most current and relevant information available. With MongoDB Atlas, financial institutions gain an unrivaled advantage in their fight against fraud, unleashing the full potential of operational data to create a robust and proactive defense system. Amazon SageMaker Canvas Amazon SageMaker Canvas revolutionizes the way business analysts leverage AI/ML solutions by offering a powerful no-code platform. Traditionally, implementing AI/ML models required specialized technical expertise, making it inaccessible for many business analysts. However, SageMaker Canvas eliminates this barrier by providing a visual point-and-click interface to generate accurate ML predictions for classification, regression, forecasting, natural language processing (NLP), and computer vision (CV). SageMaker Canvas empowers business analysts to unlock valuable insights, make data-driven decisions, and harness the power of AI without being hindered by technical complexities. It boosts collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. It brings the realm of AI/ML within reach, allowing analysts to explore new frontiers and drive innovation within their organizations. Reference Architecture The above reference architecture includes an end-to-end solution for detecting different types of fraud in the banking sector, including card fraud detection, identity theft detection, account takeover detection, money laundering detection, consumer fraud detection, insider fraud detection and mobile banking fraud detection to name a few. The architecture diagram shown here illustrates model training and near real-time inference. The operational data stored in MongoDB Atlas is written to the Amazon S3 bucket using the Triggers feature in Atlas Application Services. Thus stored, data is used to create and train the model in Amazon SageMaker Canvas. The SageMaker Canvas stores the metadata for the model in the S3 bucket and exposes the model endpoint for inference. For step-by-step instructions on how to build the fraud detections solution mentioned above with MongoDB Atlas and Amazon SageMaker Canvas, read our tutorial .

June 21, 2023