Babu Srinivasan

11 results

Driving Innovation through Data Refractoring: Empowering Applications with MongoDB Atlas and AWS Modernization Strategies

In today's fast-paced digital landscape, businesses are constantly seeking innovative ways to stay competitive and deliver exceptional user experiences. However, many organizations find themselves hindered by the burden of outdated legacy applications, technical debt, and the subsequent impact on their customers. The need of the hour is to pivot towards a solution that can revolutionize the way businesses operate: application modernization. By addressing the challenges of legacy applications, tackling technical debt, and leveraging modernization strategies, organizations can enhance customer impact, streamline processes, and confidently embrace the future. In the journey toward application modernization, selecting the right database and database design plays a crucial role. Clients have the challenges of the correct method of refractoring, fear of touching the aged legacy code base, lengthy traditional modernization process, high uncertainty of business-as-usual (BAU) support, and difficulty to modernize the data layer. To overcome these challenges, organizations are turning to modern database solutions like MongoDB Atlas, a popular NoSQL database. MongoDB offers flexibility, scalability, and agility, making it an ideal choice for modernizing applications. Its schemaless design allows for dynamic changes without disrupting functionality, and it offers horizontal scalability to handle large amounts of data and growing user demands. Cloud-native databases, like MongoDB Atlas, further enhance modernization efforts. They provide fault tolerance, high availability, and elastic scaling, enabling organizations to design resilient and scalable systems. By leveraging MongoDB Atlas, businesses can optimize performance, adapt to changing needs, and ensure data availability. In the pursuit of application modernization, carefully evaluating existing database structures, and embracing MongoDB Atlas, cloud-native databases can unlock the full potential of applications. This enables improved performance, flexibility, and scalability while minimizing the limitations posed by structured databases. MongoDB Atlas MongoDB is an Amazon Web Services (AWS) Partner with multiple AWS Competencies including Data & Analytics, Financial Services, and Government. MongoDB Atlas on AWS is the leading unified developer data platform that accelerates and simplifies how you build with data. MongoDB Atlas gives you the versatility you need to build sophisticated applications that can adapt to evolving customer and market demands. An intuitive document data model and unified query API provide a first-class developer experience, delivered in a cloud-native developer data platform built for resilience, scale, and the highest levels of data privacy and security. Power transactions, search, and application-driven analytics seamlessly through an elegant and integrated data architecture. With MongoDB Atlas, developers can focus on building their modern applications without worrying about the complexities of infrastructure provisioning, database setup, and ongoing maintenance. It provides features such as automated backups, monitoring, and built-in security measures to ensure data integrity and reliability. MongoDB Atlas empowers businesses to leverage the full potential of MongoDB's flexible document-based data model while benefiting from the scalability, availability, and ease of use offered by a cloud-based database service. MongoDB on AWS MongoDB Atlas on AWS combines best-in-class operational automation, scalability, and resilience of cloud-native services. It is available across 27+ AWS regions and has deep integrations with multiple AWS services. It enables organizations to leverage the cloud infrastructure provided by AWS to deploy, manage, and scale their MongoDB databases seamlessly. With features like automated backups, monitoring, and integration with AWS services like Amazon Elastic Compute Cloud and Amazon Simple Storage Service (Amazon S3), MongoDB on AWS empowers businesses to optimize performance, ensure data availability, and easily adapt to changing demands. Accenture Data Factory Accenture Data Factory solution is a tri-party solution from MongoDB, Accenture, and AWS leveraging MongoDB Atlas and AWS services. It’s an industry-leading solution, with highly experienced delivery teams working on the nuances of the application and database migration. It accelerates the migration process and provides 24X7 support across 27+ AWS regions. The process includes the study of the current application architecture, decomposition of the legacy applications, and migration using the standard Re-imagine, Re-factor, and Modernization of applications. The Data Factory refocuses the customer’s perspective from cost to value throughout the cloud journey. The solution offering includes the Lift & Shift of self-managed databases, Legacy Modernization (Brownfield), and Modern Application Development (Greenfield) capabilities. Lift & shift This process of Lift & Shift is generally adopted in the customer-managed database. The Lift & Shift approach leaves the application architecture largely untouched, with the exception of migrating from self-managed on-premise infrastructure components into a modern, cloud-native / cloud-managed capability. Lift & Shift provides comparatively quick benefits, limited to infrastructure and support cost reductions, but since the process undertakes no functional improvement, the same technical debt is carried to the cloud environment. Legacy modernization The Legacy Modernization process is a brown-field method of re-imagining legacy monolithic applications to harness the true potential of the cloud. It modernizes the existing application by decomposing the existing application and transforming them to the latest microservices and event-driven architecture, leveraging some of the existing legacy code bases, wherever it’s possible. Accenture’s Refractory with Data offering operates inside this Legacy Modernization capability and helps unlock significant value trapped in legacy applications and codebases. See the detailed discussion below. Modern application development Modern Application Development provides a green-field approach where new applications are developed using modernized architectures like Microservices, Event-Based, Serverless, and others. For modern applications, NoSQL platforms, especially MongoDB Atlas on AWS provide the ideal solution for the data layer. A lengthy discussion of these advantages can be found in other whitepapers but can be summarized as follows: NoSQL solutions scale linearly and nearly indefinitely for minimal risk at a very low cost compared with typical RDBMS alternatives NoSQL solutions now offer the same ACID transaction guarantees and capabilities provided by RDBMS platforms – but also the modern architecture using a service layer for all database interactions reduces the critical complexity of ACID transactions at the data layer NoSQL solutions provide seamless support and integration with DevOps and CI/CD driven deployment pipelines. NoSQL platforms like MongoDB Atlas allow updates to schema and core architectures without the pain of a database outage to make schema updates, data table reloads, and planning for rollbacks. The Refractory with data appraoch to application modernization Refractory provides a systematic approach to define a clear path from the current state of legacy applications to the desirable state of modern cloud-native microservices with modern data architectures Refractory With Data follows a 4-step process: Decompose and decouple the legacy application with functional wrappers Improve and re-imagine code within each functional unit Arrange functions into containers with clear domain boundaries Re-architect legacy data structures (RDBMS) into domain-specific data components, typically in a NoSQL architecture ideal for Microservices Decompose and decouple the legacy application Decoupling the application's components promotes loose coupling, allowing for independent development and improved maintainability and Decomposing a legacy application involves breaking it down into smaller, more manageable components, enabling gradual modernization and enhanced scalability. There are several methods commonly used for decoupling and decomposing legacy applications. Some of the methods are described below. Functional decoupling Functional decoupling of a legacy application is a process that involves isolating and separating its individual functionalities into modular and independent components. By decoupling the functions, organizations can achieve improved flexibility, maintainability, and scalability. This approach allows teams to work on specific functionalities without impacting the entire application, enabling parallel development and faster iterations. Functional decoupling reduces the dependencies and interdependencies within the legacy application, facilitating easier updates, bug fixes, and enhancements. It also paves the way for adopting modern architectural patterns, such as microservices or event-driven architecture, ultimately leading to a more agile and future-proof system. Domain driven design (DDD) DDD emphasizes understanding and modeling the core domains within a legacy application. By identifying bounded contexts and defining domain-driven architectures, businesses can decompose the application into smaller, cohesive units focused on specific domains. This method enhances modularity, and maintainability, and enables teams to work autonomously on different domains. Improve and reimagine code The data factory solution will look into each of the functional units and adopt the modernization process either through the Microservice Architecture or Event-Based architecture. Microservices architecture Decomposing the legacy application into a microservices architecture involves breaking it down into smaller, independent services that communicate with each other via APIs. Each microservice focuses on a specific business capability, making it easier to develop, test, and deploy. This approach enhances scalability, and flexibility, and enables teams to work on different services simultaneously. It’s also following multiple microservice architecture patterns that are suitable for various business demands. Some of the patterns are described below. API Gateway pattern: The API Gateway pattern acts as a single entry point for client applications to interact with multiple microservices. It consolidates requests from clients, performs authentication and authorization, and routes requests to the appropriate microservices. This pattern helps to centralize cross-cutting concerns like security, rate limiting, and request/response transformations, simplifying the client's interaction with the microservices. Service registry pattern: The Service Registry pattern is used to dynamically discover and locate microservices within a system. It involves a central registry that keeps track of registered services and their network locations. Microservices can register themselves with the registry and retrieve information about other services at runtime. This pattern promotes loose coupling between services, as they can be added or removed without requiring explicit configuration between them. Circuit breaker pattern: The Circuit Breaker pattern helps to handle faults and failures in microservice interactions. It adds a layer of protection to prevent cascading failures in a distributed system. The circuit breaker monitors requests to external services and, in case of failures or unresponsiveness, temporarily halts subsequent requests to allow the failing service to recover. This pattern enhances fault tolerance, and resilience, and prevents the system from becoming overwhelmed by failing services. Event sourcing pattern: The Event Sourcing pattern involves capturing and storing all changes to an application's state as a sequence of events. Instead of persisting in the current state, the system maintains an append-only log of events. The events can be replayed to reconstruct the system's state at any given point in time. This pattern enables auditing, and scalability, and provides a historical record of how the application's state evolved over time. Saga pattern: The Saga pattern helps maintain data consistency and coordination across multiple microservices during complex, long-running transactions. It decomposes a single transaction into a series of smaller, loosely coupled steps or compensating actions. If any step fails, compensating actions are executed to undo the changes made by previous steps. This pattern allows for eventual consistency and prevents partial updates or inconsistencies in distributed transactions. Each of these microservice patterns addresses specific challenges and provides guidelines for designing resilient, scalable, and loosely coupled microservice architectures. Implementing these patterns appropriately can help organizations build robust and manageable microservice systems. Event-driven architecture (EDA) It is an approach where components of the legacy application communicate and react to events that occur within the system. Events can trigger actions and updates across various services, enabling loose coupling and scalability. EDA promotes responsiveness, extensibility, and the ability to handle complex, real-time interactions. Each of these decomposition methods offers a unique approach to modernizing legacy applications, and organizations may choose to adopt one or a combination of them based on their specific needs and constraints. The ultimate goal is to break down the monolithic nature of the legacy application, enabling agility, scalability, and improved software development practices. Arrange functions into containers Containerization and microservice architectures go hand in hand to enable scalable and efficient application development and deployment. Containerization involves encapsulating an application and its dependencies into lightweight, isolated units known as containers. These containers provide consistent environments across different platforms, ensuring that the application runs reliably, regardless of the underlying infrastructure. Microservice architectures, on the other hand, break down an application into smaller, independent services that can be developed, deployed, and scaled independently. By combining containerization with microservices, organizations can achieve greater agility, scalability, and ease of deployment. Containers provide a portable and consistent execution environment for microservices, allowing for rapid development, deployment, and scaling of individual services. This approach promotes modularity, and fault isolation, and enhances the ability to leverage cloud-native technologies, enabling organizations to efficiently build, deploy, and manage complex applications in distributed environments. Re-architect legacy data structures The refractory framework leverages MongoDB database schema design patterns to re-architect the legacy data structures. Each of these schema patterns satisfies a set of unique business requirements. Some of the patterns are described below: Embedded data model: This pattern involves embedding related data within a single document. It is suitable for one-to-one or one-to-many relationships where the embedded data is accessed and modified together. It improves read performance as the data is retrieved in a single document access, but updates can be more complex if the embedded data needs frequent modifications. Normalized data model: In this pattern, related data is stored in separate collections and linked using references or foreign keys. It is ideal for many-to-many relationships or scenarios where data updates are frequent. While it ensures data consistency and simplifies updates, it may require additional queries to fetch related data, potentially impacting read performance. Tree structure model: This pattern is suitable for hierarchical or tree-like data structures, such as categories, organizational charts, or comment threads. It uses a parent-child relationship to represent the hierarchy, allowing easy navigation and retrieval of hierarchical data. However, maintaining integrity and performing updates across the tree can be more complex. Polymorphic data model: This pattern handles scenarios where a field can accept different data types. It allows for storing different types of data within the same field, making the schema more flexible. This pattern can simplify the schema but may require additional logic to handle different data types correctly. Bucket pattern: The bucket pattern is used to optimize queries on large collections by partitioning the data into smaller "buckets" based on specific criteria, such as time ranges or ranges of values. It helps improve query performance by reducing the amount of data scanned in a query, but it requires careful planning and consideration of the query patterns. It's important to note that the choice of schema design pattern depends on the specific requirements of the application, such as data relationships, read and write patterns, performance considerations, and scalability needs. Understanding these patterns and selecting the most appropriate one can help optimize the performance and efficiency of MongoDB database operations. Customer references We adopted the refractory model with one of the leading North America Insurance firms for modernizing their application landscape. The customer needed to modernize a huge monolithic application delivering to the needs of the customer. But with the monolith architecture the insurance firm was challenged with the agile business changes and innovation of their product. Release management was limited to only two releases per year due to the complex regression testing of the massive application spanning the mainframe and multiple technologies. The solution was originally hosted on the Mainframe supported by an RDBMS database. Following the Refractory approach the application was modernized into Microservices on MongoDB Atlas on AWS. In the process, we also created a consolidated operational data layer to optimize the business layer of their application stack The developer productivity increased by more than 70% by taking advantage of MongoDB’s integration with agile-friendly deployment processes and the number of product releases also increased considerably to 5 major and 350+ minor releases in a calendar year. Recap With the Data Factory solution, you can deploy, manage, and grow your database on AWS for customer needs without any major disruption, enabling organizations to leverage the power of data. For Tri-party solutions contact: Minal Ahuja (AWS) For MongoDB private offers contact: Olivier Zieleniecki (MongoDB) For Accenture contact: Steve Meyer (Accenture)

June 27, 2023

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

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

揭露欺骗：利用 MongoDB Atlas 和 Amazon SageMaker Canvas 的强大功能进行欺诈检测

金融服务机构面临的网络犯罪风险越来越大。备受瞩目的黑客攻击和欺诈性交易破坏了人们对该行业的信心。随着技术的发展，这些犯罪者所采用的技术也在不断发展，使得打击欺诈成为一项永恒的挑战。 现有的欺诈检测系统经常面临一个严重的限制：依赖陈旧的数据。在快节奏、不断变化的环境中，仅仅依赖历史信息就像通过后视镜开车一样。网络犯罪分子不断调整策略，迫使金融机构不得不领先一步。从数据中往往可以看到最新策略。这就是运营数据的力量所在。 通过利用实时数据，可以根据最准确、最相关的可用线索来训练欺诈检测模型。MongoDB Atlas 是一个高度可扩展且灵活的开发者数据平台，与先进的机器学习工具 Amazon SageMaker Canvas 相结合，为彻底改变欺诈检测提供了开创性的机会。通过利用运营数据，这种协同作用是主动识别和打击欺诈活动的关键，使金融机构能够在日益危险的数字环境中保护其系统和客户。 MongoDB Atlas 开发者数据平台 MongoDB Atlas 是一套以云数据库为中心的集成数据服务套件，旨在加速和简化开发者使用数据进行构建的方式。MongoDB Atlas 的面向文档的架构是金融服务组织的游戏规则改变者。它能够以灵活的模式处理海量数据，使金融机构能够轻松地实时捕获、存储和处理大量交易数据。这意味着，每一笔交易、每一次互动和每一条运营数据都可以无缝集成到欺诈检测管道中，确保根据最新的相关信息对模型进行持续训练。借助 MongoDB Atlas，金融机构在打击欺诈方面获得了无与伦比的优势，释放了运营数据的全部潜力，创建了强大且主动的防御系统。 Amazon SageMaker Canvas Amazon SageMaker Canvas 通过提供强大的无代码平台，彻底改变了业务分析师利用 AI/ML 解决方案的方式。传统上，实施 AI/ML 模型需要专门的技术专业知识，这让许多业务分析师望而却步。然而，SageMaker Canvas 提供了一个可视化的点击界面，可生成准确的 ML 预测，用于分类、回归、预测、自然语言处理 (NLP) 和计算机视觉 (CV)，从而消除了这一障碍。SageMaker Canvas 使业务分析师能够获得有价值的见解，做出数据驱动的决策，并利用 AI 的力量，而不会受到技术复杂性的阻碍。它通过跨工具共享、审查和更新 ML 模型来促进业务分析师和数据科学家之间的协作。它将 AI/ML 带入了一个触手可及的领域，使分析人员能够探索新的前沿领域，并推动组织内部的创新。 参考架构 上述参考架构包括用于检测银行业不同类型欺诈的端到端解决方案，包括银行卡欺诈检测、身份盗用检测、账户接管检测、洗钱检测、消费者欺诈检测、内部欺诈检测和手机银行欺诈检测等。 此处所示的架构图说明了模型训练和近乎实时的推理。使用 Atlas 应用程序服务中的触发器功能将存储在 MongoDB Atlas 中的操作数据写入 Amazon S3 存储桶。存储后的数据用于在 Amazon SageMaker Canvas 中创建和训练模型。SageMaker Canvas 将模型的元数据存储在 S3 存储桶中，并公开模型端点以进行推理。 有关如何使用 MongoDB Atlas 和 Amazon SageMaker Canvas 构建上述欺诈检测解决方案的分步说明， 请阅读我们的教程 。

June 21, 2023

Desmascarando a fraude: aproveitando o poder do MongoDB Atlas e do Amazon SageMaker Canvas para a detecção de fraudes

As organizações de serviços financeiros enfrentam riscos de criminosos cibernéticos crescentes. Os hacks de alto nível e as transações fraudulentas minam a confiança no setor. À medida que a tecnologia evolui, o mesmo acontece com as técnicas empregadas por esses criminosos, tornando a batalha contra a fraude um desafio perpétuo. Os sistemas de detecção de fraudes existentes geralmente enfrentam uma limitação crítica: depender de dados obsoletos. Em um cenário acelerado e em constante evolução, confiar apenas em informações históricas é semelhante a dirigir olhando para o retrovisor. Os criminosos cibernéticos adaptam continuamente suas táticas, forçando as instituições financeiras a se manterem um passo à frente. As táticas mais recentes geralmente podem ser vistas nos dados. É aí que o poder dos dados operacionais entra em ação. Ao aproveitar os dados em tempo real, os modelos de detecção de fraude podem ser treinados com base nas pistas mais precisas e relevantes disponíveis. O MongoDB Atlas, uma plataforma de dados para desenvolvedores altamente escalável e flexível, juntamente com o Amazon SageMaker Canvas, uma ferramenta avançada de aprendizado de máquina, apresenta uma oportunidade inovadora para revolucionar a detecção de fraudes. Ao aproveitar os dados operacionais, essa sinergia é a chave para identificar e combater proativamente as atividades fraudulentas, permitindo que as instituições financeiras protejam seus sistemas e seus clientes em um cenário digital cada vez mais traiçoeiro. MongoDB Atlas MongoDB Atlas , a plataforma de dados para desenvolvedores, é um conjunto integrado de serviços de dados centrado em um banco de dados em cloud projetado para acelerar e simplificar a forma como os desenvolvedores criam com os dados. A arquitetura orientada a documentos do MongoDB Atlas é um divisor de águas para as organizações de serviços financeiros. Sua capacidade de lidar com grandes quantidades de dados em um esquema flexível permite que as instituições financeiras capturem, armazenem e processem sem esforço dados transacionais de alto volume em tempo real. Isso significa que cada transação, cada interação e cada dado operacional pode ser perfeitamente integrado ao pipeline de detecção de fraudes, garantindo que os modelos sejam continuamente treinados com base nas informações mais atuais e relevantes disponíveis. Com o MongoDB Atlas, as instituições financeiras obtêm uma vantagem inigualável na luta contra a fraude, liberando todo o potencial dos dados operacionais para criar um sistema de defesa robusto e proativo. Amazon SageMaker Canvas O Amazon SageMaker Canvas revoluciona a maneira como os analistas de negócios aproveitam as soluções de IA/ML, oferecendo uma poderosa plataforma sem código. Tradicionalmente, a implementação de modelos de IA/ML exigia conhecimento técnico especializado, tornando-a inacessível para muitos analistas de negócios. No entanto, o SageMaker Canvas elimina essa barreira ao fornecer uma interface visual do tipo apontar e clicar para gerar previsões precisas de ML para classificação, regressão, previsão, processamento de linguagem natural (NLP) e visão computacional (CV). O SageMaker Canvas permite que os analistas de negócios obtenham insights valiosos, tomem decisões baseadas em dados e aproveitem o poder da IA sem serem prejudicados por complexidades técnicas. Ele aumenta a colaboração entre analistas de negócios e cientistas de dados, compartilhando, revisando e atualizando modelos de ML entre ferramentas. Ele coloca o domínio da IA/ML ao alcance, permitindo que os analistas explorem novas fronteiras e impulsionem a inovação em suas organizações. Arquitetura de referência A arquitetura de referência acima inclui uma solução completa para a detecção de diferentes tipos de fraude no setor bancário, incluindo detecção de fraude de cartão, detecção de roubo de identidade, detecção de aquisição de conta, detecção de lavagem de dinheiro, detecção de fraude do consumidor, detecção de fraude interna e detecção de fraude bancária móvel, entre outras. O diagrama de arquitetura mostrado aqui ilustra o treinamento do modelo e a inferência quase em tempo real. Os dados operacionais armazenados no MongoDB Atlas são gravados no bucket do Amazon S3 usando o recurso Triggers nos serviços de aplicativo do Atlas. Assim armazenados, os dados são usados para criar e treinar o modelo no Amazon SageMaker Canvas. O SageMaker Canvas armazena os metadados do modelo no bucket do S3 e expõe o endpoint do modelo para inferência. Para obter instruções passo a passo sobre como criar a solução de detecção de fraudes mencionada acima com o MongoDB Atlas e o Amazon SageMaker Canvas, leia nosso tutorial .

June 21, 2023

속임수 폭로: 사기 탐지를 위해 MongoDB Atlas 및 Amazon SageMaker Canvas의 강력한 기능 활용

금융 서비스 조직은 사이버 범죄자로부터 점점 더 많은 위험에 직면하고 있습니다. 세간의 이목을 끄는 해킹과 사기 트랜잭션은 업계에 대한 신뢰를 약화시킵니다. 기술이 발전함에 따라 이러한 가해자가 사용하는 기술도 발전하여 사기와의 전쟁은 끊임없는 과제가 되고 있습니다. 기존 사기 탐지 시스템은 오래된 데이터에 의존한다는 중대한 한계와 씨름하는 경우가 많습니다. 빠르게 변화하고 끊임없이 변화하는 환경에서 과거 정보에만 의존하는 것은 백미러를 보고 운전하는 것과 비슷합니다. 사이버 범죄자들은 지속적으로 전술을 변경하여 금융 기관이 한 발 앞서 나가도록 강요합니다. 최신 전술은 종종 데이터에서 확인할 수 있습니다. 여기서 운영 데이터의 힘이 발휘됩니다. 실시간 데이터를 활용하면 가장 정확하고 관련성 높은 단서에 대해 사기 탐지 모델을 학습시킬 수 있습니다. 확장성과 유연성이 뛰어난 개발자 데이터 플랫폼인 MongoDB Atlas는 고급 머신 러닝 도구인 Amazon SageMaker Canvas와 함께 사기 탐지를 혁신할 수 있는 획기적인 기회를 제공합니다. 이러한 시너지는 운영 데이터를 활용하여 사기 행위를 사전에 식별하고 대처함으로써 금융 기관이 점점 더 위험해지는 디지털 환경에서 시스템을 보호하고 고객을 보호할 수 있는 열쇠를 쥐고 있습니다. MongoDB Atlas 개발자 데이터 플랫폼인 MongoDB Atlas 는 개발자가 데이터로 구축하는 방법을 가속화하고 간소화하도록 설계된 클라우드 데이터베이스를 중심으로 하는 통합 데이터 서비스 제품군입니다. MongoDB Atlas의 문서 지향 아키텍처는 금융 서비스 조직의 판도를 바꾸고 있습니다. 유연한 스키마로 방대한 양의 데이터를 처리하는 기능을 통해 금융 기관은 대량의 트랜잭션 데이터를 실시간으로 손쉽게 캡처, 저장 및 처리할 수 있습니다. 즉, 모든 트랜잭션, 모든 상호 작용 및 모든 운영 데이터를 사기 탐지 파이프라인에 원활하게 통합하여 모델이 사용 가능한 최신 관련 정보를 지속적으로 학습할 수 있도록 합니다. 금융 기관은 MongoDB Atlas를 통해 운영 데이터의 잠재력을 최대한 활용하여 강력하고 사전 예방적인 방어 시스템을 구축함으로써 사기와의 전쟁에서 독보적인 우위를 점할 수 있습니다. Amazon SageMaker Canvas Amazon SageMaker Canvas 는 강력한 코드 없는 플랫폼을 제공하여 비즈니스 분석가가 AI/ML 솔루션을 활용하는 방식을 혁신합니다. 기존에는 AI/ML 모델을 구현하려면 전문 기술 지식이 필요했기 때문에 많은 비즈니스 분석가가 접근하기 어려웠습니다. 하지만 SageMaker Canvas는 분류, 회귀, 예측, 자연어 처리(NLP) 및 컴퓨터 비전(CV)을 위한 정확한 ML 예측을 생성하는 시각적인 포인트 앤 클릭 인터페이스를 제공하여 이러한 장벽을 제거합니다. SageMaker Canvas는 비즈니스 분석가가 기술적 복잡성에 방해받지 않으면서 가치 있는 인사이트를 얻고, 데이터 기반 의사 결정을 내리고, AI의 강력한 기능을 활용할 수 있도록 지원합니다. 여러 도구에서 ML 모델을 공유, 검토, 업데이트하여 비즈니스 분석가와 데이터 과학자 간의 협업을 강화합니다. AI/ML 영역을 가까이 가져오므로 분석가는 새로운 영역을 개척하고 조직 내에서 혁신을 주도할 수 있습니다. 참고 아키텍처 위의 참고 아키텍처에는 카드 사기 탐지, 신원 도용 탐지, 계정 탈취 탐지, 자금 세탁 탐지, 소비자 사기 탐지, 내부자 사기 탐지, 모바일 뱅킹 사기 탐지 등 은행 부문에서 다양한 유형의 사기를 탐지하기 위한 엔드 투 엔드 솔루션이 포함되어 있습니다. 여기에 표시된 아키텍처 다이어그램은 모델 학습과 실시간에 가까운 추론을 보여줍니다. MongoDB Atlas에 저장된 운영 데이터는 Atlas Application Services의 Triggers 기능을 사용하여 Amazon S3 버킷에 기록됩니다. 이렇게 저장된 데이터는 Amazon SageMaker Canvas에서 모델을 생성하고 학습하는 데 사용됩니다. SageMaker Canvas는 모델의 메타데이터를 S3 버킷에 저장하고 추론을 위해 모델 엔드포인트를 공개합니다. MongoDB Atlas 및 Amazon SageMaker Canvas로 위에서 언급한 사기 탐지 솔루션을 구축하는 방법에 대한 단계별 지침은 튜토리얼을 참조하세요 .

June 21, 2023

Smascherare l'inganno: sfruttare la potenza di MongoDB Atlas e Amazon SageMaker Canvas per il rilevamento delle frodi

Le organizzazioni di servizi finanziari devono affrontare rischi crescenti a causa dei criminali informatici. Gli attacchi hacker di alto profilo e le transazioni fraudolente minano la fiducia nel settore. Con l’evoluzione della tecnologia, evolvono anche le tecniche impiegate da questi autori di reati, rendendo la lotta contro la frode una sfida continua. I sistemi di rilevamento delle frodi esistenti spesso si scontrano con un limite fondamentale: fare affidamento su dati obsoleti. In un panorama in rapida evoluzione, affidarsi esclusivamente alle informazioni storiche è come guidare guardando nello specchietto retrovisore. I criminali informatici adattano continuamente le loro tattiche, costringendo gli istituti finanziari a stare un passo avanti. Le tattiche più recenti sono spesso visibili nei dati. È qui che entra in gioco la potenza dei dati operativi. Grazie alla possibilità di sfruttare i dati in tempo reale, i modelli di rilevamento delle frodi possono essere addestrati sulla base degli indizi più accurati e rilevanti disponibili. MongoDB Atlas, una piattaforma di dati per sviluppatori altamente scalabile e flessibile, abbinata ad Amazon SageMaker Canvas, uno strumento di apprendimento automatico avanzato, rappresenta un'opportunità innovativa per rivoluzionare il rilevamento delle frodi. Sfruttando i dati operativi, questa sinergia è la chiave per identificare e combattere in modo proattivo le attività fraudolente, consentendo agli istituti finanziari di salvaguardare i propri sistemi e proteggere i clienti in un panorama digitale sempre più insidioso. MongoDB Atlas MongoDB Atlas , la piattaforma di dati per gli sviluppatori, è una suite integrata di servizi di dati incentrata su un database cloud progettato per accelerare e semplificare il modo in cui gli sviluppatori costruiscono con i dati. L'architettura orientata ai documenti di MongoDB Atlas rappresenta una svolta per le organizzazioni di servizi finanziari. La sua capacità di gestire enormi quantità di dati in uno schema flessibile consente agli istituti finanziari di acquisire, archiviare ed elaborare senza sforzo grandi volumi di dati transazionali in tempo reale. Ciò significa che ogni transazione, ogni interazione e ogni dato operativo può essere integrato senza problemi nella pipeline di rilevamento delle frodi, assicurando che i modelli siano continuamente addestrati sulle informazioni più attuali e rilevanti disponibili. Con MongoDB Atlas, gli istituti finanziari ottengono un vantaggio ineguagliabile nella lotta contro le frodi, liberando tutto il potenziale dei dati operativi per creare un sistema di difesa solido e proattivo. Amazon SageMaker Canvas Amazon SageMaker Canvas rivoluziona il modo in cui gli analisti aziendali sfruttano le soluzioni AI/ML offrendo una potente piattaforma no-code. Tradizionalmente, l'implementazione di modelli di AI/ML richiedeva competenze tecniche specialistiche, rendendo inaccessibili le soluzioni per molti analisti aziendali. Tuttavia, SageMaker Canvas elimina questa barriera fornendo un'interfaccia visiva point-and-click per generare previsioni ML accurate per la classificazione, la regressione, le previsioni, l'elaborazione del linguaggio naturale (NLP) e la computer vision (CV). SageMaker Canvas consente agli analisti aziendali di ottenere preziose intuizioni, prendere decisioni basate sui dati e sfruttare la potenza dell'IA senza essere ostacolati da complessità tecniche. Favorisce la collaborazione tra analisti aziendali e data scientist condividendo, rivedendo e aggiornando i modelli di ML tra i vari strumenti. Porta il regno dell'AI/ML a portata di mano, consentendo agli analisti di esplorare nuove frontiere e di promuovere l'innovazione all'interno delle loro organizzazioni. Architettura di riferimento L'architettura di riferimento di cui sopra comprende una soluzione end-to-end per il rilevamento di diversi tipi di frode nel settore bancario, tra cui il rilevamento di frodi con carte di credito, il rilevamento di furti d'identità, l'acquisizione di conti, il rilevamento di riciclaggio di denaro, il rilevamento di frodi ai danni dei consumatori, il rilevamento di frodi insider e il rilevamento di frodi nel settore del mobile banking, per citarne alcuni. Il diagramma dell'architettura qui riportato illustra l'addestramento del modello e l'inferenza in tempo quasi reale. I dati operativi memorizzati in MongoDB Atlas vengono scritti nel bucket Amazon S3 utilizzando la funzione Triggers di Atlas Application Services. I dati così archiviati vengono utilizzati per creare e addestrare il modello in Amazon SageMaker Canvas. SageMaker Canvas memorizza i metadati del modello nel bucket S3 ed espone l'endpoint del modello per l'inferenza. Per istruzioni dettagliate su come costruire la soluzione di rilevamento delle frodi di cui sopra con MongoDB Atlas e Amazon SageMaker Canvas, leggi il nostro tutorial .

June 21, 2023

Démasquer les fraudes : exploiter la puissance de MongoDB Atlas et d'Amazon SageMaker Canvas pour détecter les fraudes

Les organisations de services financiers sont confrontées à des risques croissants causés par les cybercriminels. Des piratages et des transactions frauduleuses très médiatisés sapent la confiance dans ce secteur. La technologie évolue, tout comme les techniques employées par les fraudeurs, ce qui fait de la lutte contre la fraude un défi permanent. Les systèmes existants de détection des fraudes sont souvent confrontés à une contrainte majeure : ils s'appuient sur des données obsolètes. Dans un paysage en constante évolution, s'appuyer uniquement sur des informations historiques revient à conduire en regardant dans son rétroviseur. Les cybercriminels adaptent constamment leurs tactiques, ce qui oblige les institutions financières à toujours garder une longueur d'avance. Les nouvelles tactiques sont souvent identifiables dans les données. C'est là que la puissance des données opérationnelles entre en jeu. En exploitant les données en temps réel, les modèles de détection des fraudes peuvent être formés sur la base des indices les plus précis et les plus pertinents dont on dispose. MongoDB Atlas, une plateforme de données de développement hautement évolutive et flexible, associée à Amazon SageMaker Canvas, un outil d'apprentissage automatique avancé, offre une opportunité inédite de révolutionner les méthodes de détection des fraudes. En exploitant les données opérationnelles, cette synergie est la clé de l'identification proactive et de la lutte contre les activités frauduleuses, permettant aux institutions financières de sauvegarder leurs systèmes et de protéger leurs clients dans un environnement numérique de plus en plus dangereux. MongoDB Atlas MongoDB Atlas , la plateforme de données pour les développeurs, est une suite intégrée de services de données centrés autour d'une base de données cloud conçue pour accélérer et simplifier la façon dont les développeurs utilisent les données. L'architecture orientée documents de MongoDB Atlas change la donne pour les organisations de services financiers. Sa capacité à gérer des quantités massives de données selon un schéma flexible permet aux institutions financières de collecter, de stocker et de traiter sans effort et en temps réel des volumes importants de données transactionnelles. Cela signifie que chaque transaction, chaque interaction et chaque donnée opérationnelle peut être intégrée de manière transparente dans le pipeline de détection des fraudes, garantissant ainsi que les modèles sont continuellement entraînés sur la base des informations les plus récentes et les plus pertinentes à disposition. Avec MongoDB Atlas, les institutions financières acquièrent un avantage inégalé dans leur lutte contre la fraude, en libérant tout le potentiel des données opérationnelles pour créer un système de défense fiable et proactif. Amazon SageMaker Canvas Amazon SageMaker Canvas révolutionne la façon dont les analystes commerciaux exploitent les solutions IA/ML en offrant une puissante plateforme sans code. Par le passé, l'implémentation de modèles d'IA/ML nécessitait une expertise technique spécialisée, ce qui la rendait inexploitable pour de nombreux analystes d'entreprise. Cependant, SageMaker Canvas offre désormais une interface visuelle permettant de générer des prédictions ML précises pour la classification, la régression, la prévision, le traitement du langage naturel (NLP) et la vision par ordinateur (CV). SageMaker Canvas permet aux analystes commerciaux d'obtenir des informations précieuses, de prendre des décisions basées sur les données et d'exploiter la puissance de l'IA sans se heurter à des complexités techniques. Il favorise la collaboration entre les analystes commerciaux et les data scientists en partageant, révisant et mettant à jour les modèles ML des différents outils. Il permet aux analystes d'explorer de nouvelles frontières et de stimuler l'innovation au sein de leur organisation. Architecture de référence L'architecture de référence ci-dessus comprend une solution de bout en bout pour la détection de différents types de fraude dans le secteur bancaire, notamment la détection de la fraude aux cartes bancaires, des usurpations d'identité, des piratages de comptes, du blanchiment d'argent, des fraudes à la consommation, des fraudes d'initiés et des fraudes liées aux services bancaires mobiles, pour n'en nommer que quelques-unes. L'architecture présentée ici illustre le processus d'apprentissage du modèle et l'inférence en temps quasi réel. Les données opérationnelles stockées dans MongoDB Atlas sont écrites dans le compartiment Amazon S3 à l'aide de la fonctionnalité Triggers d'Atlas Application Services. Les données ainsi stockées sont utilisées pour créer et entraîner le modèle dans Amazon SageMaker Canvas. SageMaker Canvas stocke les métadonnées du modèle dans le compartiment S3 et expose l'endpoint du modèle pour l'inférence. Pour des instructions pas à pas sur la façon de mettre en place la solution de détection des fraudes mentionnée ci-dessus avec MongoDB Atlas et Amazon SageMaker Canvas, nous vous invitons à découvrir notre tutoriel .

June 21, 2023

Cómo desenmascarar engaños: aproveche el poder de MongoDB Atlas y Amazon SageMaker Canvas para la detección de fraudes

Las organizaciones de servicios financieros enfrentan riesgos cada vez más riesgos a causa de los delincuentes cibernéticos. Los trucos de alto perfil y las transacciones fraudulentas socavan la confianza en la industria. A medida que la tecnología evoluciona, también lo hacen las técnicas empleadas por estos perpetradores, lo que causa que la batalla contra el fraude sea un desafío perpetuo. Los sistemas existentes de detección de fraudes a menudo ofrecen una limitación crítica: confiar en datos obsoletos. Con un panorama que evoluciona a un ritmo vertiginoso, basarse únicamente en la información histórica es como conducir mirando por el retrovisor. Los delincuentes cibernéticos adaptan continuamente sus tácticas, obligando así a las instituciones financieras a mantenerse un paso adelante. Con frecuencia, las tácticas más actualizadas a pueden verse en los datos. Ahí es donde entra en juego el poder de los datos operativos. Al aprovechar los datos en tiempo real, los modelos de detección de fraude se pueden entrenar con las pistas más precisas y relevantes disponibles. MongoDB Atlas, una plataforma de datos para desarrolladores altamente escalable y flexible, junto con Amazon SageMaker Canvas, una herramienta avanzada de aprendizaje automático, presenta una oportunidad innovadora para revolucionar la detección de fraudes. Al aprovechar los datos operativos, esta sinergia es la clave para identificar y combatir de manera proactiva las actividades fraudulentas, lo que permite a las instituciones financieras salvaguardar sus sistemas y proteger a sus clientes en un panorama digital cada vez más desleal. MongoDB Atlas MongoDB Atlas , la plataforma de datos para desarrolladores consiste en un conjunto integrado de servicios de datos centrado en una base de datos en la nube que fue diseñada para acelerar y simplificar la forma en que los desarrolladores hacen su trabajo con los datos. La arquitectura orientada a documentos de MongoDB Atlas cambia las reglas del juego para las organizaciones de servicios financieros. Su capacidad para manejar cantidades masivas de datos en un esquema flexible permite a las instituciones financieras capturar, almacenar y procesar sin ningún tipo de esfuerzo datos transaccionales de gran volumen y en tiempo real. Esto significa que cada transacción, cada interacción y cada dato operativo se puede integrar perfectamente en el proceso de detección de fraudes, lo que garantiza que los modelos se entrenen continuamente con la información más actualizada y relevante disponible. Con MongoDB Atlas, las instituciones financieras obtienen una ventaja inigualable en su lucha contra el fraude, al liberar todo el potencial de los datos operativos para crear un sistema de defensa sólido y proactivo. Amazon SageMaker Canvas Amazon SageMaker Canvas revoluciona la forma en que los analistas empresariales aprovechan las soluciones de IA/AA al ofrecer una potente plataforma sin código. Tradicionalmente, la implementación de modelos de IA/AA requería conocimientos técnicos especializados, lo que la hacía inaccesible para muchos analistas de negocio. Sin embargo, SageMaker Canvas elimina esta barrera ya que proporciona una interfaz visual con solo apuntar y hacer clic para generar predicciones precisas de AA para clasificación, regresión, pronóstico, procesamiento de lenguaje natural (NLP) y visión artificial (CV). SageMaker Canvas permite a los analistas de negocios obtener información valiosa, tomar decisiones basadas en datos y aprovechar el poder de la IA sin verse obstaculizados por complejidades técnicas. Impulsa la colaboración entre los analistas de negocios y los científicos de datos al compartir, revisar y actualizar modelos de AA en todas las herramientas. Pone el reino de la IA/AA al alcance de la mano, lo que permite a los analistas explorar nuevas fronteras e impulsar la innovación dentro de sus organizaciones. Arquitectura de referencia La arquitectura de referencia anterior incluye una solución de extremo a extremo para detectar distintos tipos de fraude en el sector bancario, como la detección de fraudes con tarjetas, la detección de robos de identidad, la detección de apropiaciones de cuentas, la detección de blanqueo de capitales, la detección de fraudes a consumidores, la detección de fraudes con información privilegiada y la detección de fraudes en la banca móvil, por citar algunos ejemplos. El diagrama de arquitectura que se muestra aquí ilustra la capacitación modelo y la inferencia casi en tiempo real. Los datos operativos almacenados en MongoDB Atlas se escriben en el bucket de S3 de Amazon mediante la función Activadores en Atlas Application Services. Por lo tanto, los datos se utilizan para crear y entrenar el modelo en Amazon SageMaker Canvas. SageMaker Canvas almacena los metadatos del modelo en el bucket de S3 y expone el punto de enlace del modelo para su inferencia. Para obtener instrucciones paso a paso sobre cómo crear la solución de detección de fraudes mencionada anteriormente con MongoDB Atlas y Amazon SageMaker Canvas, lea nuestro tutorial .

June 21, 2023

Betrug enttarnen: Nutzung der Leistungsfähigkeit von MongoDB Atlas und Amazon SageMaker Canvas für die Betrugserkennung

Finanzdienstleistungsunternehmen sehen sich wachsenden Risiken durch Cyberkriminelle ausgesetzt. Aufsehen erregende Hacks und betrügerische Transaktionen untergraben das Vertrauen in die Branche. Mit der Weiterentwicklung der Technologie entwickeln sich auch die von den Tätern angewandten Techniken weiter, sodass der Kampf gegen Betrug eine ständige Herausforderung darstellt. Bestehende Betrugserkennungssysteme haben oft mit einer entscheidenden Einschränkung zu kämpfen: Sie verlassen sich auf veraltete Daten. In einer schnelllebigen und sich ständig weiterentwickelnden Landschaft ist es so, als würde man beim Fahren nur in den Rückspiegel blicken – man verlässt sich ausschließlich auf historische Informationen. Cyberkriminelle passen ihre Taktiken ständig an und zwingen Finanzinstitute dazu, immer einen Schritt voraus zu sein. Die neuesten Taktiken lassen sich oft an den Daten ablesen. Hier kommt die Leistungsfähigkeit von operativen Daten ins Spiel. Durch die Nutzung von Echtzeitdaten können Betrugserkennungsmodelle anhand der genauesten und relevantesten verfügbaren Hinweise trainiert werden. MongoDB Atlas, eine hoch skalierbare und flexible Plattform für Entwicklerdaten, bietet in Verbindung mit Amazon SageMaker Canvas, einem fortschrittlichen Tool für maschinelles Lernen, eine bahnbrechende Möglichkeit, die Betrugserkennung zu revolutionieren. Durch die Nutzung von Betriebsdaten ist diese Synergie der Schlüssel zur proaktiven Erkennung und Bekämpfung von betrügerischen Aktivitäten. So können Finanzinstitute ihre Systeme sichern und ihre Kunden in einer zunehmend tückischen digitalen Landschaft schützen. MongoDB Atlas MongoDB Atlas , die Plattform für Entwicklerdaten, ist eine integrierte Suite von Datenservices rund um eine Cloud-Datenbank, die die Arbeit von Entwicklern mit Daten beschleunigen und vereinfachen soll. Die dokumentenorientierte Architektur von MongoDB Atlas ist ein entscheidender Vorteil für Finanzdienstleistungsunternehmen. Die Fähigkeit, riesige Datenmengen in einem flexiblen Schema zu verarbeiten, ermöglicht es Finanzinstituten, mühelos große Transaktionsdatenmengen in Echtzeit zu erfassen, zu speichern und zu verarbeiten. Das bedeutet, dass jede Transaktion, jede Interaktion und jedes Stück operativer Daten nahtlos in die Betrugserkennungspipeline integriert werden kann, wodurch sichergestellt wird, dass die Modelle kontinuierlich auf den aktuellsten und relevantesten verfügbaren Informationen trainiert werden. Mit MongoDB Atlas verschaffen sich Finanzinstitute einen unschlagbaren Vorteil im Kampf gegen Betrug, indem sie das volle Potenzial von Betriebsdaten nutzen, um ein robustes und proaktives Abwehrsystem zu schaffen. Amazon SageMaker Canvas Amazon SageMaker Canvas revolutioniert die Art und Weise, wie Business-Analysten KI/ML-Lösungen nutzen, indem es eine leistungsstarke No-Code-Plattform bietet. Traditionell erforderte die Implementierung von KI/ML-Modellen spezielles technisches Fachwissen, was sie für viele Unternehmensanalysten unzugänglich machte. SageMaker Canvas beseitigt diese Hürde jedoch, indem es eine visuelle Point-and-Click-Benutzeroberfläche zur Verfügung stellt, mit der Sie präzise ML-Vorhersagen für Klassifizierung, Regression, Vorhersage, natürliche Sprachverarbeitung (Natural Language Processing, NLP) und Computer Vision (CV) erstellen können. Mit SageMaker Canvas können Geschäftsanalysten wertvolle Erkenntnisse gewinnen, datengestützte Entscheidungen treffen und die Möglichkeiten der KI nutzen, ohne durch technische Komplexität behindert zu werden. Es fördert die Zusammenarbeit zwischen Geschäftsanalysten und Datenwissenschaftlern durch die gemeinsame Nutzung, Überprüfung und Aktualisierung von ML-Modellen über verschiedene Tools hinweg. Es bringt die Welt der KI/ML in greifbare Nähe und ermöglicht es Analysten, neue Grenzen zu erforschen und Innovationen in ihren Unternehmen voranzutreiben. Referenzarchitektur Die oben beschriebene Referenzarchitektur umfasst eine End-to-End-Lösung für die Erkennung verschiedener Arten von Betrug im Bankensektor, einschließlich der Erkennung von Kartenbetrug, Identitätsdiebstahl, Kontoübernahmen, Geldwäsche, Verbraucherbetrug, Insiderbetrug und Betrug beim mobilen Banking, um nur einige zu nennen. Das hier gezeigte Architekturdiagramm veranschaulicht das Modelltraining und die Inferenz in nahezu Echtzeit. Die in MongoDB Atlas gespeicherten operativen Daten werden mithilfe der Trigger-Funktion in Atlas Application Services in den Amazon S3-Bucket geschrieben. Die so gespeicherten Daten werden zum Erstellen und Trainieren des Modells in Amazon SageMaker Canvas verwendet. Der SageMaker Canvas speichert die Metadaten für das Modell im S3-Bucket und stellt den Modell-Endpunkt für die Inferenz zur Verfügung. Eine Schritt-für-Schritt-Anleitung, wie Sie die oben erwähnte Betrugserkennungslösung mit MongoDB Atlas und Amazon SageMaker Canvas aufbauen, finden Sie in unserem Tutorial .

June 21, 2023

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 and AWS AppSync today. Sign up for MongoDB Atlas on AWS Marketplace Today

June 20, 2023

Modernize your GraphQL APIs with MongoDB Atlas and AWS AppSync

Modern applications typically need data from a variety of data sources, which are frequently backed by different databases and fronted by a multitude of REST APIs. Consolidating the data into a single coherent API presents a significant challenge for application developers. GraphQL emerged as a leading data query and manipulation language to simplify consolidating various APIs. GraphQL provides a complete and understandable description of the data in your API, giving clients the power to ask for exactly what they need — while making it easier to evolve APIs over time. It complements popular development stacks like MEAN and MERN , aggregating data from multiple origins into a single source that applications can then easily interact with. MongoDB Atlas: A modern developer data platform MongoDB Atlas is a modern developer data platform with a fully managed cloud database at its core. It provides rich features like native time series collections, geospatial data, multi-level indexing, search, isolated workloads, and many more — all built on top of the flexible MongoDB document data model. MongoDB Atlas App Services help developers build apps, integrate services, and connect to their data by reducing operational overhead through features such as hosted Data API. The Atlas Data API allows developers to easily integrate Atlas data into their cloud apps and services over HTTPS with a flexible, REST-like API layer. AWS AppSync: Serverless GraphQL and pub/sub APIs AWS AppSync is an AWS managed service that allows developers to build GraphQL and Pub/Sub APIs. With AWS AppSync, developers can create APIs that access data from one or many sources and enable real-time interactions in their applications. The resulting APIs are serverless, automatically scale to meet the throughput and latency requirements of the most demanding applications, and charge only for requests to the API and by real-time messages delivered. Exposing your MongoDB Data over a scalable GraphQL API with AWS AppSync Together, AWS AppSync and MongoDB Atlas help developers create GraphQL APIs by integrating multiple REST APIs and data sources on AWS. This gives frontend developers a single GraphQL API data source to drive their applications. Compared to REST APIs, developers get flexibility in defining the structure of the data while reducing the payload size by bringing only the attributes that are required. Additionally, developers are able to take advantage of other AWS services such as Amazon Cognito, AWS Amplify, Amazon API Gateway, and AWS Lambda when building modern applications. This allows for a severless end-to-end architecture, which is backed by MongoDB Atlas serverless instances and available in pay-as-you-go mode from the AWS Marketplace . Paths to integration AWS AppSync uses data sources and resolvers to translate GraphQL requests and to retrieve data; for example, users can fetch MongoDB Atlas data using AppSync Direct Lambda Resolvers. Below, we explore two approaches to implementing Lambda Resolvers: using the Atlas Data API or connecting directly via MongoDB drivers . Using the Atlas Data API in a Direct Lambda Resolver With this approach, developers leverage the pre-created Atlas Data API when building a Direct Lambda Resolver. This ready-made API acts as a data source in the resolver, and supports popular authentication mechanisms based on API Keys, JWT, or email-password. This enables seamless integration with Amazon Cognito to manage customer identity and access. The Atlas Data API lets you read and write data in Atlas using standard HTTPS requests and comes with managed networking and connections, replacing your typical app server. Any runtime capable of making HTTPS calls is compatible with the API. Figure 1:   Architecture details of Direct Lambda Resolver with Data API Figure 1 shows how AWS AppSync leverages the AWS Lambda Direct Resolver to connect to the MongoDB Atlas Data API. The Atlas Data API then interacts with your Atlas Cluster to retrieve and store the data. MongoDB driver-based Direct Lambda Resolver With this option, the Lambda Resolver connects to MongoDB Atlas directly via drivers , which are available in multiple programming languages and provide idiomatic access to MongoDB. MongoDB drivers support a rich set of functionality and options , including the MongoDB Query Language, write and read concerns, and more. Figure 2:   Details the architecture of Direct Lambda Resolvers through native MongoDB drivers Figure 2 shows how the AWS AppSync endpoint leverages Lambda Resolvers to connect to MongoDB Atlas. The Lambda function uses a MongoDB driver to make a direct connection to the Atlas cluster, and to retrieve and store data. The table below summarizes the different resolver implementation approaches. Table 1:   Feature comparison of resolver implementations Setup Atlas Cluster Set up a free cluster in MongoDB Atlas. Configure the database for network security and access. Set up the Data API. Secret Manager Create the AWS Secret Manager to securely store database credentials. Lambda Function Create Lambda functions with the MongoDB Data APIs or MongoDB drivers as shown in this Github tutorial . AWS AppSync setup Set up AWS Appsync to configure the data source and query. Test API Test the AWS AppSync APIs using the AWS Console or Postman . Figure 3:   Test results for the AWS AppSync query Conclusion To learn more, refer to the AppSync Atlas Integration GitHub repository for step-by-step instructions and sample code. This solution can be extended to AWS Amplify for building mobile applications. For further information, please contact partners@mongodb.com .

November 23, 2022