Dan Mckean

4 results

3 Ways MongoDB EA Azure Arc Certification Serves Customers

One reason more than 50,000 customers across industries choose MongoDB is the freedom to run anywhere—across major cloud providers, on-premises in data centers, and in hybrid deployments. This is why MongoDB is always working to meet customers where they are. For example, many customers choose MongoDB Atlas (which is available in more than 115 cloud regions across major cloud providers) for a fully managed experience. Other customers choose MongoDB Enterprise Advanced (EA) to self-manage their database deployments to meet specific on-premises or hybrid requirements. To that end, we’re pleased to announce that MongoDB EA is one of the first certified Microsoft Azure Arc-enabled Kubernetes applications, which provides customers even more choice of where and how they run MongoDB. Customer adoption of Azure Arc has grown by leaps and bounds. This new certification, and the launch of MongoDB EA as an Arc-enabled Kubernetes application on Azure Marketplace , means that more customers will be able to leverage the unparalleled security, availability, durability, and performance of MongoDB across environments with the centralized management of their Kubernetes deployments. We are very excited to have MongoDB available for our customers on the Azure Marketplace. By extending Azure Arc’s management capabilities to your MongoDB deployments, customers gain the benefit of centralized governance, enhanced security, and deeper insights into database performance. Azure Arc makes hybrid database management with MongoDB efficient and consistent. Collaboration between MongoDB and Microsoft represents an opportunity for many of our customers to further accelerate their digital transformation when building enterprise-class solutions with Azure Arc. Christa St Pierre, Partner Group Manager, Azure Edge Devices, Microsoft Here are three ways the launch of MongoDB EA on Azure Marketplace for Arc-enabled Kubernetes applications gives customers greater flexibility. 1. MongoDB EA supports multi-Kubernetes cluster deployments, simplifies management MongoDB Enterprise Advanced seamlessly integrates market-leading MongoDB capabilities along with robust enterprise support and tools for self-managed deployments at any scale. This powerful solution includes advanced automation, comprehensive auditing, strong authentication, reliable backup, and insightful monitoring capabilities, all of which work together to ensure security compliance and operational efficiency for organizations of any size. The relationship between MongoDB and Kubernetes is one of strong synergy. With Kubernetes, MongoDB EA really can run anywhere, such as a single deployment spanning on-premises and more than one public cloud Kubernetes cluster. Customers can use the MongoDB Enterprise Kubernetes Operator, a key component of MongoDB Enterprise Advanced, to simplify the management and automation of self-managed MongoDB deployments in Kubernetes. This includes tasks like creating and updating deployments, managing backups, and integrating with various Kubernetes services. The ability of the MongoDB Enterprise Kubernetes Operator to deploy and manage MongoDB deployments that span multiple Kubernetes clusters significantly enhances resilience, improves disaster recovery, and minimizes latency by allowing data to be co-located closer to where it is needed, ensuring optimal performance and reliability. 2. Azure Arc complements MongoDB EA, providing centralized management While MongoDB Enterprise Advanced is already among a select group of databases capable of operating across multiple Kubernetes clusters , it is now also supported in Azure Arc-enabled Kubernetes environments. Azure Arc enables the standardized management of Kubernetes clusters across various environments—including in Azure, on-premises, and even other clouds—while harnessing the power of Azure services. Azure Arc accomplishes this by extending the Azure control plane to standardize security and governance across a wide range of resources and locations. For instance, organizations can centrally monitor all of the Azure Arc-enabled Kubernetes clusters using Azure Monitor for containers , or they can enforce threat protection at scale using Microsoft Defender for Kubernetes. This centralized control significantly reduces the complexity of managing Kubernetes clusters running anywhere, as customers can oversee all resources and apply consistent security and compliance policies across their hybrid environment. 3. Customers can leverage the resilience of MongoDB EA and the centralized governance of Azure Arc Together, these solutions empower organizations to build robust applications across a wide array of environments, whether on-premises or in multi-cloud settings. The combination of MongoDB Enterprise Advanced and the MongoDB Enterprise Operator simplifies the deployment of MongoDB across Kubernetes clusters, allowing organizations to fully leverage enhanced resilience and geographic distribution that surpasses the capabilities of a single Kubernetes cluster. Azure Arc further enhances this synergy by providing centralized management for all of these Kubernetes clusters, regardless of where they are running; for customers running entirely in the public cloud, we recommend using MongoDB’s fully managed developer data platform, MongoDB Atlas. If you’re interested in learning more, we invite you to explore the Azure Marketplace listing for MongoDB Enterprise Advanced for Arc-enabled Kubernetes applications. Please note that aside from use for evaluation and development purposes, this offering requires the purchase of a MongoDB Enterprise Advanced subscription. For licensing inquiries, we encourage you to reach out to MongoDB at https://mongodb.prakticum-team.ru/contact to secure your license and to begin harnessing the full potential of these powerful solutions.

November 19, 2024

Introducing: Multi-Kubernetes Cluster Deployment Support

Resilience and scalability are critical for today's production applications. MongoDB and Kubernetes are both well known for their ability to support those needs to the highest level. TA single Kubernetes cluster is typically limited to a single region, so to better enable developers using MongoDB and Kubernetes, we’ve introduced a series of updates and capabilities that makes it possible to manage MongoDB across multiple Kubernetes clusters. Since those Kubernetes clusters can be located in different regions, this offers new levels of resilience and control over where your data lives. In addition to the previously released support for running MongoDB replica sets and Ops Manager across multiple Kubernetes clusters, we're excited to announce the public preview release of support for Sharded Clusters spanning multiple Kubernetes clusters (GA to follow in November 2024). Support for deployment across multiple Kubernetes clusters is facilitated through the Enterprise Kubernetes Operator. As a recap for anyone unaware, the Enterprise Operator automates the deployment, scaling, and management of MongoDB clusters in Kubernetes. It simplifies database operations by handling tasks such as configuration, resizing, upgrades, and failover, whilst ensuring consistent performance and reliability in the Kubernetes environment. Multi-Kubernetes cluster deployment support enhances availability, resilience, and scalability for critical MongoDB workloads, empowering developers to efficiently manage these workloads within Kubernetes. This approach unlocks the highest level of availability and resilience by allowing shards to be located closer to users and applications, increasing geographical flexibility and reducing latency for globally distributed applications. Deploying replica sets across multiple Kubernetes clusters MongoDB replica sets are engineered to ensure high availability, data redundancy, and automated failover in database deployments. A replica set consists of multiple MongoDB instances—one primary and several secondary nodes—all maintaining the same dataset. The primary node handles all write operations, while the secondary nodes replicate the data and are available to take over as primary if the original primary node fails. This architecture is critical for maintaining continuous data availability, especially in production environments where downtime can be costly. Support for deploying MongoDB replica sets across multiple Kubernetes clusters helps remove the Kubernetes cluster itself as a single point of failure. Deploying MongoDB replica sets across multiple Kubernetes clusters enables you to distribute your data, not only across nodes in the Kubernetes cluster, but across different clusters and geographic locations, ensuring your deployments operational (even if one or more Kubernetes clusters or locations fail) and facilitating faster disaster recovery. To learn more about how to deploy replica sets across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation . Sharding MongoDB across multiple Kubernetes clusters While replica sets duplicate data for resilience (and higher read rates), MongoDB sharded clusters divide the data up between shards, each of which is effectively a replica set, providing resilience for each portion of the data. Crucially, this also helps your database handle large datasets and higher-throughput operations since each shard has a primary member handling write operations to that portion of the data; this allows MongoDB to scale up the write throughput horizontally, rather than requiring vertical scaling of every member of a replica set. In a Kubernetes environment, each shard can now be deployed across multiple Kubernetes clusters, giving every shard higher resilience in the event of a loss of a Kubernetes cluster or an entire geographic location. This also offers the ability to locate shards or their primaries in the same region as the applications or users accessing that portion of the data, reducing latency and improving user experience. Sharding is particularly useful for applications with large datasets and those requiring high availability and resilience as they grow. Support for sharding MongoDB across multiple Kubernetes clusters is currently in public preview and will be generally available in November. Deploying Ops Manager across multiple Kubernetes clusters Ops Manager is the self-hosted management erver that supports automation, monitoring, and backup of MongoDB on your own infrastructure. Ops Manager's most critical function is backup of MongoDB deployments, and deploying it across multiple Kubernetes clusters greatly improves resilience and disaster recovery for your MongoDB deployments in Kubernetes. With Ops Manager distributed across several Kubernetes clusters, you can ensure that backups of deployments remain robust and available, even if one Kubernetes cluster or site fails. Furthermore, it allows Ops Manager to efficiently manage and monitor MongoDB deployments that are themselves distributed across multiple clusters, improving central oversight of your deployments. To learn more about how to deploy Ops Manager across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation . To leverage multi-Kubernetes-cluster support, you can get started with the Enterprise Kubernetes Operator .

October 10, 2024

MongoDB Enterprise Advanced in Google Distributed Cloud Hosted

Today, we’re excited to strengthen our "run anywhere" approach and deepen our relationship with Google Cloud by announcing that MongoDB Enterprise Advanced is now available for use within Google Distributed Cloud Hosted (GDC Hosted). "Google Cloud is happy to welcome MongoDB as a preferred partner for our Google Distributed Cloud Hosted product," said Rohan Grover, Director of Product for GDC Hosted. "MongoDB's powerful document database aligns with our data analytics focus, empowering our shared customers to unlock the full potential of their sensitive data in an air-gapped private cloud." GDC Hosted is Google Cloud’s air-gapped private cloud that does not require connectivity to Google Cloud or the public internet to manage the infrastructure, services, APIs, or tooling. GDC Hosted enables public sector organizations and regulated enterprises to address strict data residency and security requirements, while continuing to deliver innovation to their users. MongoDB Enterprise Advanced combines the power of MongoDB —– the leading NoSQL, document-oriented database that supports a variety of data structures — with an industry-leading offering catering to customers with the most advanced security and data sovereignty needs. As a flexible and scalable solution, MongoDB allows diverse datasets to be stored in a schemaless format, ensuring easy data manipulation and real-time analytics. Together, GDC Hosted and MongoDB Enterprise Advanced offer a solution that enables users to scale their operations while adhering to the strictest data governance and security standards. The bridge between GDC Hosted and MongoDB Enterprise Advanced is Kubernetes : GDC Hosted is built on Kubernetes allowing teams to self-manage MongoDB through the use of the MongoDB Enterprise Kubernetes Operator. The MongoDB Enterprise Kubernetes Operator is the only officially supported way to run Enterprise Advanced deployments of MongoDB in Kubernetes. To enable customers to manage deployments within their environment of choice (GDC Hosted in this case), the operator works in conjunction with the MongoDB self-hosted Ops Manager, which the operator can also install and manage in Kubernetes. This gives customers the ability to deploy, monitor, back up, and scale MongoDB. The Enterprise Operator drastically simplifies both the setup and day-two operations like upgrades, making it possible to run MongoDB in Kubernetes with far less Kubernetes expertise. Creation and configuration of database deployments can be managed via a Git repo, saving developers from needing the permissions or knowledge needed to work directly with Kubernetes. By leveraging the Enterprise Kubernetes Operator, users can manage their MongoDB deployments with even greater power and scale, and maximize their investment in both MongoDB and Google Cloud. GDC Hosted is built to meet high regulatory, durability, and availability requirements, which aligns with MongoDB Enterprise Advanced’s commitment to giving users the tools and support they need to have complete control over the management and security of their self-managed MongoDB environments. While MongoDB Atlas is the best way to run MongoDB on Google Cloud, MongoDB Enterprise Advanced in GDC Hosted is the best option for teams that need absolute self-managed control over data governance and compliance, while still allowing for scalability. Once you have GDC Hosted up and running, you can get started with MongoDB Enterprise Advanced through the MongoDB Enterprise Advanced listing in the GDC Hosted Marketplace. Alternatively, teams can access MongoDB Enterprise Advanced through the Google Cloud Platforms Marketplace. MongoDB customers who want to get started using Enterprise Advanced in their GDC Hosted environments will need to sign up for a MongoDB Enterprise Advanced license through MongoDB first. For more information, reach out to gcp@mongodb.com . To learn more about the Enterprise Kubernetes Operator, visit our documentation . To learn more about Enterprise Advanced, visit our product page or download the latest version .

February 6, 2024

Leveraging MongoDB Atlas in your Internal Developer Platform (IDP)

DevOps, a portmanteau of “Developer” and “Operations,” rose to prominence around the early 2010s and established a culture of incorporating automated processes and tools designed to deliver applications and services to users faster than the traditional software development process. A significant part of that was the movement to "shift left" by empowering developers to self-serve their infrastructure needs, in theory offering them more control over the application development lifecycle in a way that reduced the dependency on central operational teams. While these shifts towards greater developer autonomy were occurring, the proliferation of public clouds, specific technologies (like GitHub, Docker, Kubernetes, Terraform), and microservices architectures entered the market and became standard practice in the industry. As beneficial as these infrastructure advancements were, these technical shifts added complexity to the setups that developers were using as a part of their application development processes. As a result, developers needed to have a more in-depth, end-to-end understanding of their toolchain, and more dauntingly, take ownership of a growing breadth of infrastructure considerations. This meant that the "shift left" drastically increased the cognitive load on developers, leading to inefficiencies because self-managing infrastructure is time-consuming and difficult without a high level of expertise. In turn, this increased the time to market and hindered innovation. Concurrently, the increasing levels of permissions that developers needed within the organization led to a swath of compliance issues, such as inconsistent security controls, improper auditing, unhygienic data and data practices increased overhead which ate away at department budgets, and incorrect reporting. Unsurprisingly, the desire to enable developers to self-serve to build and ship applications hadn't diminished, but it became clear that empowering them without adding friction or a high level of required expertise needed to become a priority. With this goal in mind, it became clear that investment was required to quickly and efficiently abstract away the complexities of the operational side of things for developers. From this investment comes the rise of Platform Engineering and Internal Developer Platforms (whether companies are labeling it as such or not). Platform engineering and the rise of internal developer platforms Within a developer organization, platform engineering (or even a central platform team) is tasked with creating golden paths for developers to build and ship applications at scale while keeping infrastructure spend and cognitive load on developers low. At the core of the platform engineering ethos is the goal of optimizing the developer experience to accelerate the delivery of applications to customers. Like teaching someone to fish, platform teams help pave the way for greater developer efficiency by providing them with pipelines that they can take and run with, reducing time to build, and paving the way for greater developer autonomy without burdening developers with complexity. To do this, platform teams strive to design toolchains and workflows based on the end goals of the developers in their organization. Therefore, it’s critical for the folks tasked with platform engineering to understand the needs of their developers, and then build a platform that is useful to the target audience. The end result is what is often (but not exclusively) known as an Internal Developer Platform. What is an IDP? An IDP is a collection of tools and services, sourced and stitched together by central teams to create golden paths for developers who will then use the IDP to simplify and streamline application building. IDPs reduce complexity and lower cognitive load on developers - often by dramatically simplifying the experience of configuring infrastructure and services that are not a direct part of the developer's application. They encourage developers to move away from spending excess time managing the tools they use and allow them to focus on delivering applications at speed and scale. IDPs enable developers the freedom to quickly and easily build, deploy, and manage applications while reducing risk and overhead costs for the organization by centralizing oversight and iteration of development practices. An IDP is tailored with developers in mind and will often consist of the following tools: Infrastructure platform that enabled running a wide variety of workloads with the highest degree of security, resilience, and scalability, and a high degree of automation (eg. Kubernetes) Source code repository system that allows teams to establish a single source of truth for configurations, ensuring version control, data governance, and compliance. (eg. Github, Gitlab, BitBucket) Control interface that enables everyone working on the application to interact with and manage its resources. (eg. Port or Backstage) Continuous integration and continuous deployment (CI/CD) pipeline that applies code and infrastructure configuration to an infrastructure platform. (eg. ArgoCD, Flux, CircleCI, Terraform, CloudFormation) Data layer that can handle changes to schemas and data structures. (eg. MongoDB Atlas) Security layer to manage permissions in order to keep compliance. Examples of this are roles-based compliance tools or secrets management tools (eg. Vault). While some tools have overlap and not all of them will be a part of a specific IDP, the goal of platform engineering efforts is to build an IDP for their developers that is tightly integrated with infrastructure resources and services to maximize automation, standardization, self-service, and scale for developers, as well as maximizing security whilst minimizing overhead for the enterprise. While there will be many different terms that different organizations and teams use to refer to their IDP story, at its core, an IDP is a tailored set of tech, tools, and processes , built and managed by a central team, and used to provide developers with golden paths that enable greater developer self-service, lower cognitive load, and reduce risk. How does MongoDB Atlas fit into this story? Developers often cite working with data as one of the most difficult aspects of building applications. Rigid and unintuitive data technologies impede building applications and can lead to project failure if they don’t deliver the data model flexibility and query functionality that your applications demand. A data layer that isn’t integrated into your workflows slows deployments, and manual operations are a never-ending drag on productivity. Failures and downtime lead to on-call emergencies – not to mention the enormous potential risk of a data breach. Therefore, making it easy to work with data is critical to improving the developer experience. IDPs are in part about giving developers the autonomy to build applications. For this reason, MongoDB’s developer data platform is a natural fit for an IDP because it serves as a developer data platform that can easily fit into any team’s existing toolstack and abstracts away the complexities associated with self-managing a data layer. MongoDB’s developer data platform is a step beyond a traditional database in that it helps organizations drive innovation at scale by providing a unified way to work with data that address transactional workloads, app-driven analytics, full-text search, vector search, stream data processing, and more, prioritizing an intuitive developer experience and automating security, resilience, and performance at scale. This simplification and broad coverage of different use cases make a monumental difference to the developer experience. By incorporating MongoDB Atlas within an IDP, developer teams have a fully managed developer data platform at their disposal that enables them to build and underpin best-in-class applications. This way teams won’t have to worry about adding the overhead and manual work involved in self-hosting a database and then building all these other supporting functionality that come out of the box with MongoDB Atlas. Lastly, MongoDB Atlas can be hosted on more cloud regions than any other cloud database in the market today with support for AWS, Azure, and Google Cloud. How can I incorporate MongoDB Atlas into my IDP? MongoDB Atlas’ Developer Data Platform offers many ways to integrate Atlas into their IDP through many tools that leverage the MongoDB Atlas Admin API. The Atlas Admin API can be used independently or via one of these tools/integrations and provides a programmatic interface to directly manage and automate various aspects of MongoDB Atlas, without needing to switch between UIs or incorporate manual scripts. These tools include: Atlas Kubernetes Operator HashiCorp Terraform Atlas Provider AWS CloudFormation Atlas Resources Atlas CDKs Atlas CLI Atlas Go SDK Atlas Admin API With the Atlas Kubernetes Operator, platform teams are able to seamlessly integrate MongoDB Atlas into the current Kubernetes deployment pipeline within their IDP allowing their developers to manage Atlas in the same way they manage their applications running in Kubernetes. First, configurations are stored and managed in a git repository and applied to Kubernetes via CD tools like ArgoCD or Flux. Then, Atlas Operator's custom resources are applied to Atlas using the Atlas Admin API and support all the building blocks you need, including projects, clusters, database users, IP access lists, private endpoints, backup, and more. For teams that want to take the IaC route in connecting Atlas to their IDP, Atlas offers integrations with HashiCorp Terraform and AWS CloudFormation which can also be used to programmatically spin up Atlas services off the IaC integrations built off the Atlas Admin API in the Cloud environment of their choice.. Through provisioning with Terraform, teams can deploy, update, and manage Atlas configurations as code with either the Terraform Provider or the CDKTF. MongoDB also makes it easier for Atlas customers who prefer using AWS CloudFormation to easily manage, provision, and deploy MongoDB Atlas services in three ways: through resources from the CloudFormation Public Registry, AWS Quick Starts, and the AWS CDK. Other programmatic ways that Atlas can be incorporated into an IDP are through Atlas CLI, which interacts with Atlas from a terminal with short and intuitive commands and accomplishes complex operational tasks such as creating a cluster or setting up an access list interactively Atlas Go SDK which provides platform-specific and Go language-specific tools, libraries, and documentation to help build applications quickly and easily Atlas Admin API provides a RESTful API, accessed over HTTPS, to interact directly with MongoDB Atlas control plane resources. The fastest way to get started is to create a MongoDB Atlas account from the AWS Marketplace , Azure Marketplace , or Google Cloud Marketplace . Go build with MongoDB Atlas today!

January 4, 2024