MongoDB Atlas for Government Supports GCP Assured Workloads

Alex Bauer
August 20, 2024

We’re excited to announce that MongoDB Atlas for Government now supports the US regions of Google Cloud Assured Workloads, alongside existing support for AWS GovCloud and AWS US regions. This expansion offers greater flexibility and expanded support for public sector organizations and the independent software vendors (ISVs) that serve them as they modernize applications and migrate workloads to the cloud. Furthermore, MongoDB Atlas for Government is now available for purchase through the Google Cloud Marketplace.

MongoDB Atlas for Government: Driving digital transformation in the public sector

MongoDB Atlas for Government is an independent, dedicated version of MongoDB Atlas, designed specifically to meet the unique needs of the U.S. public sector and ISVs developing public sector solutions. This developer data platform provides the versatility and scalability required to modernize legacy applications and migrate workloads to the cloud, all within a secure, fully-managed, FedRAMP authorized environment. Refer to the FedRAMP Marketplace listing for additional information about Atlas for Government.

By leveraging the full functionality of MongoDB's document database and application services, Atlas for Government supports a wide range of use cases within a unified developer data platform, including Internet of Things, AI/ML, analytics, mobile development, single view, transactional workloads, and more.

Ensuring robust resilience and comprehensive disaster recovery, Atlas for Government maintains business continuity and minimizes downtime. With a ~99.995% uptime SLA, auto-scaling to handle data consumption fluctuations, and automated backup and recovery, organizations can have peace of mind that their data is always protected.

Getting started with MongoDB Atlas for Government

MongoDB Atlas for Government can be used to create database clusters deployed to a single region or spanning multiple US regions. Google Cloud Assured Workloads US regions are now supported in Atlas for Government projects tagged as “Gov regions only,” allowing for the use of both traditional Google Cloud regions as well as Assured Workloads US regions.

To get started, create a project in Atlas for Government and make sure to select 'Designate as a Gov Cloud regions-only project' during the project creation process.

Screenshot of the MongoDB Atlas platforms Create a Project function.

After creating the project, you can set up a MongoDB cluster in the GCP regions. To do this, start the cluster creation process and select GCP as the Cloud Provider, as shown in the figure below. You'll then be prompted to choose one or more GCP regions for your cluster. You can find more details on supported cloud providers and regions in the Atlas for Government documentation.

Creating multi-cloud clusters

The introduction of support for Google Cloud Assured Workloads (US regions) makes MongoDB Atlas for Government the first fully managed multi-cloud data platform authorized at FedRAMP Moderate.

This means that public sector organizations and ISVs can now deploy clusters across Google Cloud Assured Workloads US regions and AWS GovCloud regions, in addition to deploying database clusters across multiple US regions.

Screenshot of the Atlas UI where you are able to select your multi-cloud cloud regions.

Whether prioritizing performance, cost, or specific feature sets, Atlas for Government empowers teams to deploy application architectures that simultaneously take advantage of the best-of-class services from multiple cloud providers while meeting FedRAMP requirements. Multi-cloud support also provides additional resiliency and enhanced disaster recovery, safeguarding data and applications against potential service outages and failures with automatic failover.

Ensuring robust data protection and seamless continuity

MongoDB Atlas for Government now supports Google Cloud Assured Workloads US regions, expanding its multi-cloud capabilities alongside existing support for AWS GovCloud and AWS US regions. This enhancement provides public sector organizations and ISVs with the flexibility to modernize applications and migrate workloads in a secure, FedRAMP authorized environment.

With robust resilience, comprehensive disaster recovery, and a ~99.995% uptime SLA, Atlas for Government ensures data protection and business continuity. By offering a unified developer data platform for a wide range of use cases, Atlas for Government empowers teams to leverage best-in-class cloud services while meeting stringent compliance requirements.

How do I get started?

Visit our product page to learn more about MongoDB Atlas for Government. Or, read the Atlas for Government documentation to learn how to get started today.

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Find Hidden Insights in Vector Databases: Semantic Clustering

Vector databases, a powerful class of databases designed to optimize the storage, processing, and retrieval of large volume, multi-dimensional data, have increasingly been instrumental to generative AI (gen AI) applications, with Forrester predicted a 200% increase in the adoption of vector databases in 2024. But their power extends far beyond these applications. Semantic vector clustering, a technique within vector databases, can unlock hidden knowledge within your organization’s data, democratizing insights across teams. View the tutorial to get started. Mining diverse data for hidden knowledge Imagine your organization’s data as a library of diverse knowledge—a treasure trove of information waiting to be unearthed. Traditionally, uncovering valuable insights from data often relied on asking the right questions, which can be a challenge for developers, data scientists, and business leaders alike. They might spend vast amounts of time sifting through limited, siloed datasets, potentially missing hidden gems buried within the organization's vast data troves. Simply put, without knowing the right questions to ask, these valuable insights often remain undiscovered, leading to missed opportunities or losses. Enter vector databases and semantic vector clustering. A vector database is designed to store and manage unstructured data efficiently. Within a vector database, semantic vector clustering is a technique for organizing information by grouping vectors with similar meaning together. Text analysis, sentiment analysis, knowledge classification, and uncovering semantic connections between data sets—these are just a few examples of how semantic vector clustering empowers organizations to vastly improve data mining. Semantic vector clustering offers a multifaceted approach to organizational improvement. By analyzing text data, it can illuminate customer and employee sentiments, behaviors, and preferences, informing strategic decisions, enhancing customer service, and optimizing employee satisfaction. Furthermore, it revolutionizes knowledge management by categorizing information into easily accessible clusters, thereby boosting collaboration and efficiency. Finally, by bridging data silos and uncovering hidden relationships, semantic vector clustering facilitates informed decision-making and breaks down organizational barriers. For example, the business can gain significant insights from its customer interaction data which is routinely kept, classified, or summarized. Those data points (texts, numbers, images, videos, etc.) can be vectorized and semantic vector clustering applied to identify the most prominent customer patterns (the densest vector clusters) from those interactions, classifications, or summaries. From the identified patterns, the business can take further actions or make more informed decisions that they wouldn’t have been able to do otherwise. The power of semantic vector clustering So, how does semantic vector clustering achieve all this? Discover semantic structures: Clustering groups similar LLM-embedded vector sets together. This allows for fast retrieval of themes. Beyond clustering regular vectors (individual data points or concepts), clustering RAG vectors (summarization of themes and concepts) can provide superior LLM contexts compared to basic semantic search. Reduce data complexity via clustering: Data points are grouped based on overall similarity, effectively reducing the complexity of the data. This reveals patterns and summarizes key features, making it easier to grasp the bigger picture. Imagine organizing the library by theme or genre, making it easier to navigate vast amounts of information. Semantic auto-aggregation: Here is the coolest part. We can classify groups of vectors into hierarchies by effectively semantically "auto-aggregating" them. This means that the data itself “figures out” these groups and "self-organizes." Imagine a library with an efficient automated catalog system, allowing researchers to find what they need quickly and easily. Vector clustering can be used to create hierarchies, essentially "auto-aggregating" groups of vectors semantically. Think of it as automatically organizing sections of the library based on thematic connections without a set of pre-built questions. This allows you to identify patterns within a vast, semantically-diverse data within your organization. Unlock hidden insights in your vector database The semantic clustering of vector embeddings is a powerful tool to go beyond the surface of data and identify meanings that otherwise would not have been discovered. By unlocking hidden relationships and patterns, you can extract valuable insights that drive better decision-making, enhance customer experiences, and improve overall business efficiency—all enabled through MongoDB’ secure, unified, and fully-managed vector database capabilities. Check out our tutorial to learn how to get started. Head over to our quick-start guide to get started with Atlas Vector Search today. Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.

August 19, 2024
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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024