Boosting Performance and Insights with MongoDB Atlas and New Relic

Frank Sun
August 24, 2023
#Atlas

In order to keep up with the demands of the modern business landscape, organizations must prioritize monitoring and optimizing application performance. Today, we're excited to announce a powerful collaboration between MongoDB Atlas, the leading database-as-a-service platform, and New Relic, the renowned monitoring and observability solution. This integration enables users to seamlessly monitor, analyze, and optimize their MongoDB deployments with efficiency and ease.

Observability to ensure availability and uptime

Monitoring a MongoDB Atlas deployment is now simpler than ever with the integration of New Relic. By connecting the two platforms, users can effortlessly monitor the performance of their MongoDB metrics, such as latency, throughput, and error rates, directly within the New Relic user interface. With real-time visibility into the health and performance of their databases, developers, and operations teams can quickly identify potential bottlenecks and proactively address issues before they impact end-users. Similarly, administrators can get an immediate high-level view of the health and availability of their MongoDB databases.

Intelligent alerting and notifications

The combination of MongoDB Atlas and New Relic empowers users to set up intelligent alerts and notifications tailored to their specific business requirements. Leveraging New Relic's alerting capabilities, users can create custom alert policies based on performance metrics and query patterns. Whether it's a sudden increase in response times or an unexpected spike in query access patterns, teams can receive timely notifications via email, Slack, or other preferred channels, enabling them to take immediate action.

Powerful dashboarding and reporting

Users can also take advantage of comprehensive dashboarding and reporting capabilities. With customizable dashboards and rich visualizations, users can gain real-time insights into the performance of their MongoDB clusters. Additionally, New Relic's reporting tools enable teams to generate detailed reports on database performance, query analytics, and overall system health, empowering them to make data-driven decisions and track improvements over time.

By combining the strengths of these two powerful platforms, users can now unlock a new level of control and efficiency in managing their MongoDB databases. From streamlined monitoring and analysis to improved troubleshooting and enhanced collaboration, this integration leads organizations to proactively optimize their applications, ensure scalability, and deliver exceptional user experiences. With MongoDB Atlas and New Relic working cohesively, businesses can stay ahead in today's rapidly evolving digital landscape, where performance and efficiency are key differentiators.

If you’d like to see the MongoDB Atlas and New Relic integration in action, sign up for MongoDB Atlas, our cloud database service, and learn more about New Relic’s monitoring and observability capabilities.

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Powerful Generative AI Innovation Accelerates Discovery of New Molecules

Since 2018, MongoDB and Google Cloud have collaborated to revolutionize the way companies interact with their data, providing an unrivaled experience in Google Cloud regions around the world through a strategic partnership. By delivering MongoDB's popular Atlas developer data platform and deep integrations with Google's data cloud to customers, the two companies are empowering businesses to create applications at scale with unprecedented data richness, all available through the Google Cloud Marketplace . This strategic partnership is bearing fruit. In the chemical industry, for example, users are now combining AI and data mining techniques using MongoDB Atlas with Google Clouds Foundation Models to accelerate the discovery of new molecules and make the process more environmentally friendly. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The next big step in generative AI Developers can experience the powerful capabilities of MongoDB Atlas Vector Search and Google Cloud foundation models to quickly and easily build applications with AI-powered features to enable highly personalized and engaging end-user experiences. Vertex AI provides the text embedding API to generate embeddings from customer data stored in MongoDB Atlas. Atlas Vector Search is a fully managed service that simplifies the process of effectively indexing this high-dimensional embedding data within MongoDB and being able to perform fast vector similarity searches. This combined with Google’s PaLM can be used to create advanced functionality like semantic search, classification, outlier detection, AI-powered chatbots, and text summarization - enabling developers to quickly build and scale next-generation applications. With Atlas Vector Search, developers can build intelligent applications powered by generative AI over any type of data. MongoDB Atlas Vector search combined with Google Cloud foundation models integrates the operational database, vector search, and LLMs into a single, unified, and fully managed platform. You can find out more about using Vector Search with Google Cloud foundation models from our information hub . Accelerating the discovery of new molecules with generative AI Every day brings new innovations in generative AI, and Vector Search is no exception. Developers are now using Google Cloud foundation models and MongoDB Vector Search to bring inventive applications to growing industries. In one example, MongoDB Partner Exafluence is utilizing AI and data mining techniques with MongoDB Atlas Vector Search and foundation models from Google Cloud to help joint customer Anupam Rasayan discover new molecules. India-based Anupam Rasayan is one of the leading companies engaged in the custom synthesis and manufacturing of specialty chemicals. The new platform, called Exf ChemXpert, includes configurable components for a wide variety of applications in the chemical industry, such as property prediction to help design new molecules, chemical reaction optimization to make developing molecules more environmentally friendly, and novel drug discovery to develop new treatments in the pharmaceutical industry. The home page for EXF ChemXpert, a one-stop platform that's accelerating the discovery of new molecules According to Anand Desai, Managing Director of Anupam Rasayan, this powerful new integration shows great promise. "In the world of chemistry, LLMs are potential game-changers for day-to-day product research and optimization of reaction mechanisms and operations," Desai said. "It can speed up new product and process innovations, reduce usage factors and costs, and push R&D to new heights beyond conventional methods. This transformation driven by new generative AI-powered tools will bring in a shift from conventional perspectives and benefit the chemical industry at large." In the Retrosynthesis Planner, the user asks a question about how to synthesize the chemical, acrylamide. Next steps Generative AI represents a significant opportunity for developers to create new applications and experiences and to add real business value for customers. For more about building applications on MongoDB Atlas with Google Cloud foundation models, including demos where you can see generative AI in action with MongoDB Atlas on Google Cloud, visit this special information hub . To get started running MongoDB on Google Cloud, visit the Google Marketplace .

August 24, 2023

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Better Digital Banking Experiences with AI and MongoDB

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

November 26, 2024