Getting Started With MongoDB Atlas for Cloud Manager Users

MongoDB
July 1, 2016
#Cloud

Jay Gordon is a Technical Account Manager with MongoDB and is available via our chat to discuss MongoDB Cloud Products at https://cloud.mongodb.com.

MongoDB recently released our new Database as a Service (DBaaS) platform known as Atlas. MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery – we’ve got you covered.

As a Cloud Manager user, you may want to reduce your operational overhead and move to Atlas. No longer will you need to manage server hardware or your cloud instances. You only need to select your requirements from the Atlas configuration and begin working. Authentication configuration, SSL encryption as well as monitoring and backups are all managed by MongoDB.

The very first step in this process for existing Cloud Manager users is signing up for a new group. Navigate your browser to https://cloud.mongodb.com and log in with your normal credentials, once you’ve done this you should return to your deployment page:

Next step, we’ll click “Settings” and then “My Groups” on the left side of the page, we’ll find our existing Cloud Manager groups:

As you see on the right side, there’s a green “Add Group” button. Go ahead and select this, you’ll be given options now to begin creating your MongoDB Atlas Group or another Cloud Manager Group. Today we’re going to work with Atlas. Click the “MongoDB Atlas" option:

Our buddy the Automation Robot will begin the process of setting up your new Atlas group in a few seconds! Once the Automation Robot is done you’ve now entered Atlas! Welcome to your new Clusters Page:

To start building your Atlas clusters, click the green button that says “Build a New Cluster.” If you have a promotional or coupon code, you can apply it by entering it in the bottom of the payment details form under “Coupon or Activation Code”. Click save at the bottom of the screen and you’re ready to start building something GIANT using MongoDB Atlas!

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Announcing MongoDB Atlas: Database as a Service for MongoDB

Today we're announcing MongoDB Atlas , the simplest, most robust, and most cost effective way to run MongoDB in the Cloud. Atlas is a database as a service that makes running MongoDB almost effortless, whether you run a single replica set, or a sharded cluster hosting a hundred terabytes. Simple: Using Atlas is simplicity itself. There are no servers to set up, configure, or manage, no backups to schedule, no need to set up monitoring or look for security vulnerabilities. If a server goes down in the night, the system takes care of it for you. And if something cannot be fixed automatically, our 24/7 response team is paged, not you. When you need to add more storage, run on bigger hardware, or add more shards, it's a few clicks in the UI, and Atlas takes care of the rest. Robust: MongoDB Atlas is built upon the components of our current cloud offerings, which have been responsible for managing clusters for years. The monitoring component was first released 4 years ago. Atlas comes with the most advanced backup solution for MongoDB, first released three years ago. It supports point in time recovery, powerful configuration options, and has proven its stability over years of backing up the most demanding MongoDB workloads. The brain behind managing your Atlas clusters, Automation, has been available for two years, delivering reliable cluster creation, rolling upgrades, and configuration changes for thousands of MongoDB deployments in production. So while Atlas itself is new, all of the components that comprise Atlas have been battled tested for many years. Elastic Pricing: One of the nice features of cloud products in general is elastic pricing, and we wanted to provide the same convenience for our database as a service. With Atlas you pay for exactly what you use, and we bill your credit card on a monthly basis. You can spin up a cluster for a few hours to kick the tires for just a few dollars, and run your largest, most mission-critical workloads with confidence and predictable pricing. Other Options to Manage MongoDB: MongoDB Atlas is the absolute best way to run MongoDB in the cloud. However if you do not want to run entirely in the cloud, we offer other solutions that make managing MongoDB in your chosen environment easy. If you are running a hybrid environment you should look at MongoDB Cloud Manager , and if you are fully On-Prem you should try out MongoDB Ops Manager . All three management products share the same powerful components, assuring you the same peace of mind no matter what environment you run in. Wherever you want to deploy MongoDB, we’ve got you covered. Today MongoDB Atlas is available for production on AWS, and will be launched with our strategic partners, Microsoft Azure and Google Cloud Platform in the future. MongoDB has always been about getting the database out of the way and making it easy for teams to develop their apps, and now Atlas makes doing so in the cloud easier than ever. Register below for our upcoming webinar covering MongoDB Atlas . Register now: Introducing MongoDB Atlas About the Author - Eliot Horowitz Eliot is CTO and Co-Founder of MongoDB. He is one of the core MongoDB kernel committers. Previously, he was Co-Founder and CTO of ShopWiki. Eliot developed the crawling and data extraction algorithm that is the core of its innovative technology. He has quickly become one of Silicon Alley's up and coming entrepreneurs and was selected as one of BusinessWeek's Top 25 Entrepreneurs Under Age 25 nationwide in 2006. Earlier, Eliot was a software developer in the R&D group at DoubleClick (acquired by Google for $3.1 billion). Eliot received a BS in Computer Science from Brown University.

June 28, 2016

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Empower Modern App Developers with Document Databases

Across industries, business success depends on a company’s ability to deliver new digital experiences through software. The speed at which a company can develop and deploy a new application with innovative features is a direct lever on business outcomes. Given the vital role developers play in the success of your business, it stands to reason that equipping them with the tools to maximize their productivity is in your best interest. Unfortunately, many organizations are unaware of the tax they’re placing on their development teams by using a relational database. While the relational database has been a bedrock for data-driven applications for 50 years, it was developed in an era before the internet and is a poor fit as the foundation of today’s web and mobile applications. Document databases, which have emerged over the past decade, have cemented themselves as the most popular and widely used alternative to the tabular model found in traditional relational databases. Document databases have become so powerful that even relational databases are trying to emulate them. Built around JavaScript Object Notation (JSON)–like documents, document databases are intuitive for developers to use. Instead of the rigid row-and-column structure of the relational model, document databases map documents directly to objects in code, which is how coders naturally think of and work with data. Let’s break down the key advantages to document databases in building modern applications. We’ll see why the document model’s flexibility eliminates the complex intergroup dependencies that have traditionally slowed developers down. The limitations of the relational database model Relational databases add complexity to a developer’s workload, severely hampering the velocity of work. The rigid row-and-column structure creates a mismatch between the way developers think of code and data, and how they need to store it. Additionally, while the relational model was fine in an age when most applications used a small pre-set of attributes such as last names, ZIP codes, and state abbreviations, the majority of data collected by organizations today is rich in structure. We have given names and sometimes preferred names. We have unique attributes that are relevant to only some of us: For example, people with PhDs have dissertation topics, sports lovers have favorite sports, and our families come in every conceivable shape and size. This richly structured data reflects how we actually think about the real world, and it’s very difficult to flatten, store, analyze, or query using rows and columns. With relational databases, developers can feel stuck in quicksand with changes to their applications requiring them to carefully collaborate with experts like database administrators (DBAs) who help them translate their schemas and queries to underlying relational data models to ensure that indexing strategies are appropriately employed. The layer of indirection increases cognitive load, is hard to reason about, and slows everything down. More than half of application changes require database schema modifications. Those database modifications take longer to complete than the application changes they are designed to support. You can quickly see why these complicated efforts severely slow the delivery of new software features into production. Enabling development of modern apps with document databases With the birth of the internet and the proliferation of mobile and web apps, developers’ roles evolved. The emergence of robust development frameworks, which abstracted away underlying complexity, and the rise of DevOps led organizations to consolidate developer functions. The new generation of full-stack developers wanted databases that better addressed their applications’ requirements and their ways of working with data. The founders of MongoDB recognized a need for a modern database solution while at the adtech giant DoubleClick in 2007. They still were unable to scale to the 400,000 transactions per second the business required due to the constraints of the relational model. These challenges inspired them to create a new, modern, general-purpose database. This database could address the shortcomings of the relational data model and offer a solution that developers actually wanted to use. The result was a horizontally scalable, document-based NoSQL database called MongoDB. The document database model in general, and MongoDB more specifically, addresses the limitations of relational databases in several notable ways: Intuitive data model: The documents at the center of document databases have a universal data format. JSON is a lightweight, language-independent, and human-readable format that has become a widely used standard for storing and exchanging data. These documents map directly to data structures in popular programming languages so there is no need for the additional mapping layer often used with relational databases. Because data that is accessed together is stored together, there is less code to write; developers don’t need to decompose data across tables or run joins. Flexible schema: These JSON-like documents are flexible. Each document can have its own fields, and there’s no need to pre-define the schema in the database. It can be modified at any time. That flexibility enhances developer agility. Meeting user expectations while simplifying application architectures The most innovative applications we use in our daily lives — think Netflix and Instagram — have raised user expectations for what every application should be. Today we expect applications to be: Highly responsive Able to deliver relevant information Optimized for mobile devices Secure Powered by real-time insights Continuously improved Meeting those expectations can be extremely challenging, especially for developers using relational databases. A typical data infrastructure built around a legacy relational database can trap your development team in overly complex and siloed architectures. Document databases, on the other hand, can simplify application architectures. Documents are a superset of all other data models, so developers can store and work with a variety of data types. Development teams can accommodate most of their use cases in a single data model and database The document data model can help your developers overcome the limitations of the relational model while improving their productivity and velocity. Allowing them to minimize the undifferentiated work of maintaining their infrastructure and to focus on meeting demanding user expectations. As a result, they can deliver better, more innovative applications faster than before. Click here to read the original article published on The New Stack.

June 5, 2023