MongoDB and Google Partnership Gains Momentum

Matt Asay and Abhinav Mehla
October 11, 2022
#Atlas

In April 2022 MongoDB launched a pay-as-you-go Atlas service on Google Cloud Marketplace. As we said at the time, this offering provides developers with a simplified subscription experience and gives enterprises more freedom in how they run MongoDB on Google Cloud. Since that launch, we've had many hundreds of customers sign up from a wide range of industries including Retail, Automotive, Education, Media & Entertainment, Healthcare, and more.

But that's not all that happened in the past six months.

Developers clearly love to build data-rich applications with MongoDB Atlas, and just as clearly they love to bring that data to life through Google Cloud's data services like Google BigQuery, Vertex AI, and more. To indulge that developer affection for MongoDB + Google Cloud, the two companies have been busy integrating our managed services to help customers make data smarter, more intuitive, and easier to use—wherever developers choose.

Making data smart

Modern applications must be able to automate the process of capturing and processing the data within an application. Combining real-time, operational, and embedded analytics enables a business to influence and automate decision-making for the app and provide real-time insights for the user. This year MongoDB and Google Cloud have combined to deliver best-in-class, application-level analytics.

For example, in the weeks leading up to Google Cloud Next ’22, Google Cloud and MongoDB announced integration of Google BigQuery and MongoDB Atlas, among other Google data announcements. Many enterprises turn to BigQuery for its powerful, simple approach to data warehousing needs, but applying it to data in MongoDB wasn't always straightforward. To make moving and transforming data between Atlas and BigQuery easier, the MongoDB and Google teams worked together to build Dataflow templates that make it simple to package a Dataflow pipeline for deployment. The two companies also announced the integration of Atlas and BigQuery with Vertex AI to bring the power of Google's machine learning/AI expertise to MongoDB data. Developers can access a reference architecture and demo for retail and finance fraud detection scenarios. More integrations will roll out over the coming months.

All of which is great for customers. For years customers like Universe, part of Live Nation, have used MongoDB with Google Cloud services such as Cloud Pub/Sub, Cloud Dataflow, and BigQuery to build data pipelines and more. In early 2022, Forbes, a 100-year old leader in business journalism, turned to MongoDB and Google Cloud to deliver a recommendation engine for its journalists, which uses Google Cloud's machine learning services to make suggestions to appropriate contributors. These and other customers have discovered that MongoDB's data platform and Google's data services are truly better together.

Making data intuitive

All those data smarts don't amount to much if developers can't easily make use of them. Over the past six months, MongoDB and Google Cloud have further partnered to ensure a simple, intuitive developer experience. For example, we've made it incredibly easy to deploy a serverless, MEAN stack (MongoDB, ExpressJS, AngularJS, NodeJS) application with Google Cloud Run (you can read the how-to or watch a video tutorial). Similarly, we've also combined with Vercel to make it simple to build full-stack serverless apps. Serverless means you don't need to worry about any hassle associated with managing infrastructure, and Cloud Run means deployment is also a breeze. More collaboration like this will follow, all with the goal of reducing developer friction and making it easier to use stacks that combine Google and MongoDB products together.

Additionally, we've made it straightforward for developers to extend their MongoDB applications with APIs using Google's Apigee, a platform for managing and securing their APIs. For example, developers increasingly turn to Apigee and MongoDB to help enterprises pull data from legacy systems without needing the cumbersome process of integrating legacy systems. Recently, the MongoDB connector has been released in pre-GA to help developers build their APIs with MongoDB even more quickly.

Developers love these and other integrations. For example, Conrad, a leading European retailer, needed to find a way to build an online B2B marketplace for its own and third-party products. Conrad turned to Atlas and Google Cloud. Together, the companies partnered to help Conrad shift to a microservices-based architecture and delivered a simple, fast, and comprehensive data environment. In like manner, TIM, a global fixed, mobile, cloud, and data center service provider, has leaned on Atlas and Google Cloud to create a dynamic data infrastructure, which has led to a dramatic improvement in customer satisfaction scores.

Making data omnipresent

MongoDB has always put a premium on developer flexibility, which has not only meant unparalleled support for a wide variety of languages, frameworks, etc., but also flexibility in deployment, including multicloud.

Google, for its part, has been a leader in multicloud with Anthos, a platform that enables enterprises to manage GKE clusters and workloads running on virtual machines across environments. It's a way for developers to build once and deploy anywhere, including at the edge, in the data center, or on another cloud, yet with a single cloud control plane. Very cool. Among other benefits, this is a great way for enterprises to meet regulatory and data sovereignty requirements. It is not, however, the only way enterprises can attain that benefit with MongoDB and Google Cloud. As recently announced, MongoDB and Google Cloud have collaborated to give European customers additional choice in where they can securely keep their data, by making MongoDB available on the T-Systems Sovereign Cloud powered by Google Cloud.

Finally, MongoDB and Google Cloud have announced the availability of MongoDB Enterprise Advanced on Google Cloud Marketplace. As much as developers love cloud, sometimes they have the need to self-manage MongoDB. With this listing we together offer that freedom.

Now is the time to give it a try

MongoDB and Google keep giving developers increasingly rich ways to make use of data with operational, application-centered analytics and ML/AI, while also serving up a wide array of choices of where to run those applications. There are many reasons to run MongoDB Atlas on Google Cloud, and one of the easiest is with our self-service, pay-as-you-go listing on Google Cloud Marketplace. Please give it a try and let us know what you think.

Try our self-service, pay-as-you-go listing on Google Cloud Marketplace today.

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MongoDB World Recap: Why Serverless is the Architecture Developers Have Been Waiting For

Serverless architecture is growing rapidly for good reasons. Developers, for the most part, do not enjoy provisioning or managing infrastructure. For applications with intermittent traffic and long idle times, provisioning becomes a moving target. Overprovision and you risk paying for idle resources. Underprovision and you risk slow response times, poor UX, and high app abandonment rates. Serverless architecture abstracts away server, storage, and network provisioning, plus management so developers can focus on building differentiating features and creating great app experiences. Enough developers and IT organizations are embracing serverless architecture that uptake is expected to grow from $7 billion in 2020 to $37 billion by 2028. History and Evolution of Serverless The concept of serverless is older than its name. At this year's MongoDB World conference, Jeremy Daly , GM of Serverless Cloud at Serverless Inc., provided a brief history of serverless architecture—before it was called serverless—and explained why today's implementations are different from those early iterations. Serverless architecture represents the evolution of server environments from virtualization to containerization to cloud computing. In his presentation, Daly traces the beginning of serverless to the launch of Amazon Simple Storage Service ( Amazon S3 ) in 2006. Then came services such as Amazon’s CloudWatch, Simple Notification Service (SNS), CloudFront, Route 53, CloudFormation, DynamoDB, and Kinesis, which were all serverless solutions before the term existed. In 2014, Amazon released what it called "functions as a service" in the form of AWS Lambda, which was the first serverless solution to see widespread adoption. But limitations with these early iterations prevented serverless from really taking off. The primary drawback was cold starts. "If something isn't pre-provisioned, then when you request it, it has to set up a container or function to make that available to respond to your request," Daly explained. "That's still a thing today but to a much lesser degree." He also cited resource restrictions and limitations—such as the inability to call a Lambda function with an HTTP API request or connect to virtual private clouds and access a database—and limited orchestration workflows as reasons why developers were slow to adopt serverless. The Serverless Future Serverless solutions have evolved significantly from those early iterations. Over the past several years, new services have come from AWS, Google, and Azure. And now MongoDB has announced the general availability of MongoDB Atlas Serverless at World 2022. Today's solutions solve many of the issues that existed early on. According to Daly, today's serverless solutions share five common traits: No server management — Serverless eliminates routine administrative tasks such as having to SSH into a Linux box or provision Amazon Elastic Compute Cloud (Amazon EC2) instances. Flexible scaling — Although there are autoscaling groups in Amazon EC2, there's still a minimum amount that you must provision. With current serverless implementations, you can and should be able to scale down to zero. Pay for value — Sometimes referred to as "pay for use," this could entail paying for storage in a database or provisioning concurrency to eliminate cold starts, but ultimately, you're paying for the value of the services you're using. High availability — Services are automatically provisioned across multiple availability zones for redundancy. Event driven — When something happens, like a change in the database, it triggers a workflow, such as creating a new user account. While the automation and functionality are there, developers will still need to know how to use the services to take advantage of serverless. They have to know how to use different cloud services and SaaS solutions, infrastructure and cloud architecture, build and deployment pipelines, monitoring and observability, and security and compliance. These are things that a DevOps team used to handle, but now are more likely to fall on the shoulders of developers. Regardless of the responsibilities on developers to learn how to use the functionality, Daly says serverless is the future of how developers will build apps. That's because all the functionality can be spun up in an independent stack without having to worry about services that anyone else is running. Any developer can have their own version of the application that they can build off of and test with. This provides fast, high fidelity feedback loops, it enables isolated stacks for different parts of the business, and it ensures that development environments are running the same resources as production. Daly also points out that serverless architecture doesn't replace the database as the backbone of the application. Although it's hard to build a serverless database, MongoDB Atlas for Serverless has figured it out, he said. Daly cites the key characteristics developers need from a serverless database: Fast and responsive (no cold starts) Scale up and down quickly Integration with serverless tools Cloud flexibility and proximity (close to your application) Consumption-based pricing (pay only for what you're using) MongoDB Atlas Serverless MongoDB Product Manager Kevin Jernigan, who co-presented with Daly, put a finer point on delivering the requirements developers are looking for in a serverless database. To create a serverless database in Atlas, the process takes only a few steps and your database spins up in seconds. "What you have is an endpoint that will scale up and down automatically based upon the workload," Jernigan said. "It will scale down to zero when you're not using it." That essentially means you're not getting billed for anything except the storage you're using. And there's no cold start penalty. "There's always infrastructure there ready to respond to the next call you make to your database endpoint. We always have infrastructure running, waiting to respond," Jernigan said. Jernigan listed several capabilities that differentiate MongoDB Atlas Serverless from other solutions in the market. Atlas Serverless includes the full power of MongoDB, including the flexibility of the document model. There are no scaling trade-offs so you don't have to worry about cold starts when you scale to zero. It's also available with tiering-based pricing models with discounts for higher usage. There's also deployment flexibility that allows you to move workloads back and forth between serverless and dedicated infrastructure. And you can deploy in all of the major public cloud providers. To watch the complete session from MongoDB World 2022, Serverless: The Future of Application Development . To learn more about MongoDB Atlas Serverless, visit the serverless page on our website.

October 10, 2022
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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