Multi-Cloud Data Resilience with MongoDB Atlas

Steve Jurczak
October 10, 2023

MongoDB Atlas is architected to ensure that data remains safe and secure at all times, offering automated database resilience against hardware failures and regional outages. One of the reasons why MongoDB Atlas is able to provide high levels of resilience and availability is because it's the only developer data platform that's available on all three major public cloud platforms, AWS, Microsoft Azure, and Google Cloud. In fact, deploying Atlas to multiple cloud providers or regions is a simple matter of choosing how many nodes you want to deploy and in which cloud providers. And, once you're in the cloud, you can span databases cross-cloud without having to worry about setting up complicated ETL processes, so you're never locked into one cloud provider or running the risk of concentrating all your data in a single location.

By utilizing Atlas to distribute data across multiple clouds, businesses can quickly and painlessly achieve high service levels for critical applications with virtually no latency. In the event of an outage, the self-healing process kicks in — automatically electing a secondary member to take the reins within seconds without operations to the database being affected — all without any manual intervention.

Having access to multiple regions across the world provides businesses with the flexibility to adhere to data sovereignty requirements without needing to compromise on availability; cloud providers that offer just one region in a specific geography can leave users vulnerable to system disruptions, but multi-cloud clusters enable organizations to deploy additional nodes in regions not provided by their primary cloud provider. You also have the option, should you need it, of using another cloud provider in countries where a provider may only have one data center in a given region.

Geo-resilience in Atlas

MongoDB Atlas puts you in control of where your data is stored, with more than 110 regions across AWS, Google Cloud, and Microsoft Azure — to ensure your managed databases are close enough to your application servers for fast response times.

Atlas is designed to ensure maximum uptime, no matter which region or cloud provider you're using. It provides built-in geographic resilience for multi-zone, multi-region, and multi-cloud clusters with built-in data resilience features:

  • Atlas takes proactive measures to ensure the resilience of single-region clusters, automatically distributing replica set members across different cloud availability zones for maximum protection and maximum uptime.
  • Atlas helps businesses stay resilient in the face of regional failure by leveraging multi-region clusters to replicate data across geographic boundaries and keep operations running smoothly.
  • For added protection and peace of mind, multi-cloud clusters provide the perfect solution to address cloud provider failure and ensure data replication across multiple clouds.

With AWS Availability Zones, Google Regions and Zones, and Azure Availability Zones, each independent zone is made up of one or more discrete data centers, all equipped with redundant power, and networking.

A cloud region refers to the actual geographical site within a cloud service provider's infrastructure where a cluster or replica set is deployed. Regardless of the number of zones present in a cloud region, MongoDB Atlas will always deploy replica sets with at least three members to ensure the highest levels of availability and data durability.

Atlas gives you complete flexibility when it comes to configuring multi-region protection. A multi-region cluster can be hosted in multiple regions within a single cloud provider or multiple regions across multiple cloud providers.

Cloud provider disruptions can take various forms, from relatively minor capacity constraints to devastating outages that can wreak havoc on your application deployments. To reduce the risk of a major outage, organizations should consider the benefits of distributing their data across multiple clouds to maximize database and application resilience.

With multi-cloud clusters, you can easily access the powerful and unique tools and services within AWS, Google Cloud, and Azure — giving you global reach, low-latency performance, regional data security, and resilient data replication and migration. Plus, Atlas takes the hassle out of the equation, automatically distributing your data across clouds for maximum fault tolerance and giving you the freedom to explore cross-cloud migration options at any time.

Multi-cloud clusters provide the same features as single-cloud, multi-region clusters — such as continuous cloud backups, automated data tiering, and workload isolation for data analytics and visualization — but they also offer the added advantage of increased cross-cloud resilience.

For more information on multi-cloud features in MongoDB Atlas, download our Data Resilience Strategy with MongoDB Atlas whitepaper.

Find out more about deploying multi-cloud clusters from our documentation.

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Building AI with MongoDB: How Metaphor Data Uses Atlas Vector Search to Change the World Through Data

Since announcing MongoDB Atlas Vector Search in preview back in June, we’ve already seen rapid adoption from developers building a wide range of AI-enabled apps. Today we're highlighting another customer who has increased efficiency while removing architectural complexity by adopting Atlas Vector Search. Metaphor is a search and discovery tool built for data scientists, data engineers, and AI practitioners. The company’s mission is to empower individuals and companies of all types to change the world through data. Metaphor is the next evolution of the Data Catalog with fully automated support for Data Governance, Data Literacy, and Data Enablement using an intuitive user interface. We recently caught up with Mars Lan, Co-founder and CTO to learn more about the company’s journey with MongoDB and their adoption of Atlas Vector Search. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Tell us a little bit about your company and what you and the team are building We’re an early-stage startup with a mission to empower individuals and organizations to change the world through data. We refer to ourselves as the social platform for data and have a range of products that support both data teams but also data consumers. Our main product is a SaaS Data Catalog that enables governance and enablement of data across the organization. We’re a small team of around 15 or so with a keen focus on product and engineering. The company was founded about 2.5 years ago. What role does search play at the company and where did your search story begin? Well, I will start by saying that we almost ended up having a very different story to tell you than what actually ended up transpiring! We started off our journey using DocumentDB and Elasticsearch on AWS for our database and search needs. After some time we ran into some scalability issues that caused us to evaluate (and eventually move to) MongoDB Atlas for our database needs. When we saw MongoDB offered Atlas Search which was based on the same underlying Lucene technology we got very excited and began the process of migrating our search efforts over to Atlas — and this eventually laid the groundwork for adopting Atlas Vector Search later on. So starting with those initial search needs, what got you excited about Atlas Search with MongoDB? What were your use cases? We started to face a significant amount of maintenance and upkeep associated between our database and Elasticsearch. We previously had to build data pipelines, so if something changed in the database, it would also change in search. Once we eventually migrated everything to MongoDB Atlas Search, we no longer had to manage those pipelines. This resulted in lower latency and less likelihood of bugs, which excited our team. The other component to this was the scalability disconnect of having two different systems. We realized if we ever needed to spin up more storage or compute, we could just spin up a larger MongoDB cluster and get that extra scalability right away with the Atlas platform. Of course one less thing to worry about is also a huge benefit — Elasticsearch is not the easiest thing to manage, so having it all in MongoDB was another big plus for us. How did you initially learn about Atlas Vector Search and what piqued your interest? We started experimenting with Pinecone as the AI stuff really started to explode a while back, just to try out the tool, as one of our interns had initially started playing around with it. It turns out not to be cost-effective to spin up a Pinecone instance for each customer, and quite difficult to scale up due to API throttling. After some time, we started looking around for other vendors for vector search. However, once we learned that MongoDB had Vector Search we got excited at the prospect of being able to use our existing tech stack for this additional functionality. It quickly became a no-brainer to us — since we knew we were going to move everything to Atlas, it became obvious we should just consolidate everything there, so we ended up migrating to Atlas Vector Search for all of our semantic search needs. This means one query API, one set of dependencies, and build in sync, all in a single platform. What were the key factors that made you pull the trigger and adopt Atlas Vector Search? What were the problems you were trying to solve? So one key unlock for us was the semantic search side of things, where someone can ask a natural language question and get a natural language answer. This is a much more preferable user experience for us compared to your Google-style keyword searches. From day one we always wanted to best serve our core customer the engineer, but another huge constituency for us is the business or non-technical audience. These folks prefer a tool that is more intuitive to use. To best serve them we have a first-class integration into Slack and Microsoft Teams, so they can ask a question and don't have to go to another place or switch tools to get that answer. We didn’t always have the capability to do the natural language question and response, but with Atlas Vector Search this now becomes possible. Using Vector Search we now have the ability to ask the Slack bot questions like “where can I find this type of data” or “where is this one table on revenue from last quarter and who is using it” and get a natural language response back. One of the key considerations for us when looking at vendors was cost - but not just cost in terms of what shows up on an invoice. I would rather scale one system and get benefits on both (search and vector search). We saw that having to scale two systems independently was just not going to be very efficient in the long run. Can you talk about some of the initial benefits you’ve seen so far both on the Atlas Search side as well as with Vector Search specifically? How do you think about and quantify these benefits? Well one obvious thing that stands out on the search side is increased speed and being able to move quickly. MongoDB in general has a great developer experience. Our data model tends to be highly complex documents, and all the metadata tends to be highly structured and complex, so the MongoDB model fits us very well. In terms of productivity, it’s never an exact science. I will say that with the adoption of Atlas we were able to keep our engineering team size relatively constant while serving many more customers and scale our development efforts faster — so we probably saw a 2X - 3X increase in productivity. One last item of note. We adopt the most rigorous security practices because we deal with so much customer data, so we want to ensure the highest security possible. We chose to have dedicated MongoDB clusters per customer, so every customer’s data is totally isolated from each other. When we were on Pinecone, this meant spinning up a new Pinecone pod for each customer, which would be both really hard to do and not at all financially viable. Because we are centralizing this all under MongoDB, it becomes so much easier - you can dynamically scale your cluster sizes up and down depending on the needs or requirements of small vs. large customers. There’s not the sort of waste you’d get with multiple discrete systems. Getting started A big thank you to Mars and the entire Metaphor Data team for sharing more about their story and use of Atlas Vector Search. Want to learn more? Head over to our quick-start guide to get started with Atlas Vector Search today. And if you’re a startup building with AI please check out our MongoDB AI Innovators program for Atlas credits, one-on-one technical advice, access to our partner network, and more!

October 10, 2023
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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