MongoDB and AWS: How a decade-old collaboration got even better in 2022
Developers select MongoDB because it makes building with data for almost any class of application easy and fast for them. They select Amazon Web Services (AWS) because it offers a comprehensive and broadly adopted cloud platform, offering more than 200 fully featured services. Bringing together MongoDB Atlas on AWS helps developers build and ship higher quality applications faster and scale them further. MongoDB has collaborated with AWS for close to a decade now, but 2022 has seen dramatic growth in both the quantity and quality of our joint activities, resulting in a strategic collaboration agreement announced earlier this year. Our collaboration spans joint product engineering and integration so MongoDB Atlas is a first-party service on AWS, and also extends to making it easy for customers to procure MongoDB Atlas on AWS. In 2022, we have worked more closely together than ever before. In this post, we'll cover what we've achieved, and how our customers benefit. If at any point you want to stop reading about the partnership and experience it in action, we invite you to get started for free with MongoDB's fully managed, pay-as-you-go listing on the AWS Marketplace . Delivering an outstanding customer experience Since re:Invent 2021, MongoDB and AWS have jointly seen an explosion in customer success, with MongoDB for Startups becoming one of the most widely used offerings in the AWS Activate program after we launched in July. And, since launching in the AWS Marketplace with pay-as-you-go pricing in December 2021, MongoDB Atlas has become one of the most popular self-service listings, with well over 1,000 customers. More broadly, we've seen our AWS Marketplace business show triple-digit growth through significant, mutual investments across engineering, sales, and marketing. We've also found great success working with AWS' Workload Migration and Proof of Concept programs, helping many new customers accelerate their migration to MongoDB Atlas on AWS over the past 12 months. Additionally, while MongoDB works closely with AWS across the globe, we devoted increased attention to Europe this past year, resulting in a considerable increase in customer adoption. As a result, AWS named us their AWS Marketplace Partner of the Year - EMEA in November 2022. One way that we've helped to accelerate such customer success is by making it easier to procure MongoDB Atlas on AWS. Over the past year, MongoDB and AWS have significantly simplified the purchasing experience for customers. We did this across a few key areas. One thing customers love about buying through AWS Marketplace is how seamless it makes the purchasing experience. However, historically this has been slowed somewhat for MongoDB customers by the need to agree to separate legal terms. Starting in November 2022, however, all Atlas on AWS customers purchasing through the AWS Marketplace Self Service listing use AWS Marketplace’s Standard Contract for Marketplace (SCMP) terms and conditions rather than MongoDB Cloud Terms of Service, thereby further reducing friction to getting productive, faster, with MongoDB. Close product collaboration Behind these improvements to our joint purchasing experience were significant improvements to how MongoDB Atlas integrates with key AWS services. MongoDB has long worked seamlessly with core AWS services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3), and more recently has collaborated with AWS to ensure tight integration with AWS container services like Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Elastic Container Service (Amazon ECS), AWS serverless technologies like AWS Lambda, Amazon Eventbridge, and AWS Fargate; and edge computing services like AWS Wavelength . Over the past year, however, we've delved more deeply into AWS machine learning services (Amazon Comprehend, Amazon Kendra, Amazon Lex, etc.), AWS AppSync, Amazon Forecast, AWS Elastic Beanstalk, and more. In addition to direct integrations with AWS services, we made it simpler for customers to use MongoDB with important joint partners such as Datadog, Databricks, and Confluent. For Datadog, we improved MongoDB Atlas App Service to support forwarding logs on AWS to Datadog, thereby improving observability through real-time log analytics. With Databricks, we announced MongoDB as a data source within a Databricks notebook, thereby offering data practitioners an easier, more curated experience for connecting Databricks to MongoDB Atlas data. And with Confluent, we strengthened our integrations to help developers easily build robust, reactive data pipelines that stream events between applications and services in real time. Through innovations to the purchasing process and the product experience, we've helped make thousands of customers successful running MongoDB on AWS. Some joint customers, like Unqork , are upending entire industries with innovative approaches to technology and business. Others, like Volvo's Connected Solutions business , rely on MongoDB and AWS to scale their fleet management solution from tens of millions to billions of daily events. Other recent customers include Verizon , Marsello , GLS , and Shopline . Get started with MongoDB Atlas on AWS You needn't take our word for it, however. With just a few clicks — and no risk — you can get started for free with MongoDB Atlas on AWS . There's no upfront commitment, and if you choose to continue to build with MongoDB on AWS, you only pay for what you use.
10 years of MongoDB customers at AWS re:Invent
MongoDB has attended AWS re:Invent since its inception in 2012. A key reason for this is, of course, to help strengthen our partnership with AWS, which really began in 2015 and significantly expanded in March 2022 with a global, strategic collaboration agreement. But an even more fundamental reason for MongoDB's continued presence at AWS re:Invent over the years is the opportunity to engage with our many joint customers. Several MongoDB customers have been featured in re:Invent keynotes over the years. In fact, looking back at the customers AWS chose to feature in its keynotes, it's hard to find examples that are not MongoDB customers. Earlier this year, AWS celebrated 10 years of re:Invent by showcasing an equal number of "memorable customer moments" from the re:Invent mainstage. It was a great way to reaffirm AWS Leadership Principle #1 (Customer Obsession). It was also a great way to shine a light on the great things MongoDB's customers are doing. Rather than rewind on the many MongoDB customers spotlighted at re:Invent, let's look at those AWS called "most memorable" in its blog. All in on cloud Back in 2015, Capital One CIO Rob Alexander took to the re:Invent stage to discuss Capital One's "all in" approach to cloud. "We’re either using or experimenting with nearly every AWS service," Alexander said. What he didn't say, but which the company has been quite public about over the years, was how Capital One uses MongoDB in tandem with AWS services. A few months after Alexander's re:Invent comments, Capital One's Oron Gill Haus spoke at MongoDB World on Hygieia , the company's open source DevOps dashboard. Hygieia, built on MongoDB, provides the foundation for the company's attempts to reimagine banking. Haus detailed why MongoDB is so critical to Capital One's need to innovate quickly on customers' behalf, stressing how the variety and velocity of data makes MongoDB an ideal solution: We get data in from all different kinds of sources and formats, and we get it at different times. Now, what we have to do is predict the future and how you're planning on using the data. That's where traditional databases fall down. That's where you'll see MongoDB. We want to have the ability to find insights and be able to react quickly to those insights. Years later, Capital One advertises hundreds of jobs for those with MongoDB experience. (Hint: You may need to know how to roll back a MongoDB query for some of those jobs.) Capital One is doing impressive work with MongoDB, but it's not alone in its use of MongoDB for financial services. Goldman Sachs, Citi, Barclays, BBVA, Charles Schwab, FICO, HSBC, and Intuit are just a few MongoDB customers that have spoken publicly of how and why they use MongoDB. And, yes, some of these companies you may remember from the re:Invent main stage over the years. MongoDB to the Moon! Years before NASA Jet Propulsion Laboratory (JPL) took to the re:Invent stage (2016), the U.S. public agency was running MongoDB throughout NASA . By 2018, MongoDB was involved in the hugely interesting NASA Deep Space Network (DSN), a primary resource for communications and navigation for NASA's and partner agencies' interplanetary space missions. NASA had recently upgraded its decades-old infrastructure to base its modern Loading Analysis and Planning Software (LAPS) on Linux and MongoDB. LAPS, as a scientific paper details , "is responsible for long-term planning and forecasting, including studies and analysis of new missions, changed mission requirements, downtime, and new or changed antenna capabilities." Around the same time, and a key part of DSN operations, NASA was also looking for ways to improve the efficiency of operating antennas across the globe. The heart of this initiative was NASA's Link Complexity and Maintenance software (LCM), which stores all pertinent data in MongoDB. Hence, while it might not be accurate to say that MongoDB runs on the Moon, it would be true to say MongoDB helps NASA manage space missions to the Moon—and beyond. Can you hear me now? "[I]t’s just a massive moment for us at Verizon,” declared Hans Vestberg, chairman and CEO of Verizon, at re:Invent in 2019. He was talking about the company's partnership with AWS to deliver 5G network edge computing using AWS Wavelength. What wasn't said in the keynote, but that Robert Belson, Principal Engineer, Corporate Strategy, Verizon, explained , is that the vision was incomplete without MongoDB. “Verizon 5G Edge is a mobile edge computing platform, which embeds popular hyperscaler compute and storage, such as AWS Wavelength, at the edge of our 4G and 5G networks so application builders can extend existing workloads using the same popular services they know and love," he explained. “However, certain services, such as databases, are not natively supported, which is where [MongoDB] Atlas and Realm come into play by creating unprecedented flexibility for the developer and the end customer.” As we've described, Verizon decided that a comprehensive data platform was needed to make its 5G edge computing dream a reality. So Verizon integrated Atlas Functions with the Verizon edge discovery service to help direct 5G mobile clients to the topologically closest database instance across a customer’s edge deployment. In tandem, Verizon has overlaid a data persistence layer using MongoDB Realm, thereby enabling personalized experiences to extend to the network edge. Verizon is also using Atlas Device Sync and Realm to ensure the seamless synchronization of data between devices, the cloud and edge-of-network, online and offline. Customers love MongoDB + AWS Beginning to see a pattern here? While not every customer highlighted by AWS at re:Invent is a MongoDB customer, many are, including the few for which we've been able to provide some detail. Others include Epic Games, which runs its wildly popular game Fortnite on MongoDB ; or Volkswagen, which uses MongoDB throughout its web applications and in its Car Net service ; or Siemens, which runs MongoDB at the heart of its Monet system to provide monitoring, controlling, and remote management of field devices for advanced energy management services. This year while watching the various customers take the stage in re:Invent keynotes, keep in mind that they're also very likely a MongoDB customer, because customers that seek the agility and performance of AWS also tend to like how MongoDB's flexible data model enables them to do much more with their data. Interested in learning more? The best way might be to try fully managed MongoDB Atlas for free. You can get started now .
Start on Your Journey to Operationalize AI-Enhanced Real-Time Applications with MongoDB and Databricks
MongoDB and Databricks have succeeded in two complementary worlds: For MongoDB , the focus is making the world of data easy for developers building applications. For Databricks, the focus is helping enterprises to unify their data, analytics, and AI by combining a data lake's flexibility with the openness, performance, and governance of a data warehouse. Traditionally, these operational and analytical functions have existed in separate domains built by different teams and serving different audiences. Though some will pretend a data warehouse can unify such disparate data and systems, the reality is this approach leaves you making false trade-offs where your developers, your data scientists, and, ultimately, your applications and customers suffer. Data warehouses are not designed to serve consumer-facing applications at scale and process machine learning in real time. It takes the unique application-serving layer of a MongoDB database, combined with the scale and real-time capabilities of a lakehouse, such as Databricks, to automate and operationalize complex and AI-enhanced applications at scale. We observed that a large and growing population of joint customers has for years enabled the flow of data between our two platforms to run real-time businesses and enable a world of application-driven analytics, using MongoDB Connector for Apache Spark . So we asked ourselves: How could we make that a more seamless and elegant experience for these customers? Today we're announcing that Databricks now features MongoDB as a data source within a Databricks notebook , thereby enabling data practitioners with an easier, more curated experience for connecting Databricks with MongoDB Atlas data. This notebook experience makes it simpler for enterprises to deliver real-time analytics, handle complex data warehouse/BI workloads, and to operationalize AI/ML pipelines using the MongoDB Spark Connector . In turn, developer and data teams can collaborate more closely on building a new generation of app-driven intelligence. MongoDB and Databricks are committed to further improve our integration in the coming months. In this post, we'll explain how Databricks can be used as a real-time processing layer for data on MongoDB Atlas using the Spark Connector, extending MongoDB's built-in data processing capabilities like our aggregation framework . We'll also cover how to use Databricks' MongoDB notebook to make this even easier. In future posts we'll outline how to use MongoDB Atlas and Databricks Delta Lake to build sophisticated AI/ML pipelines. Live application data plus the data lakehouse MongoDB Atlas is a fully-managed developer data platform that powers a wide variety of workloads - supporting everything from simple CRUD operations to sophisticated data processing pipelines for analytics and transformation - all with a common query interface. With MongoDB Atlas you can isolate operational and analytical workloads using dedicated analytical nodes. Analytics nodes are read-only nodes that can be exclusively targeted by your queries Let's look at an example. Assume you have long-running analytical queries that you want to run against your cluster and your operations team does not want these queries competing for resources with your regular operational workload. To address this, you add an analytics node to your cluster and then target it in your connection string using an Atlas replica set tag. You can connect to the analytical nodes to run sophisticated aggregation queries, BI and reporting workloads using the Atlas SQL interface , visualize your data using MongoDB Charts , or run Spark jobs using MongoDB’s Spark Connector. For more complex data science and warehousing analytical queries, many enterprises choose the Databricks Lakehouse Platform . Enterprises can also benefit from enriching MongoDB data with data from other internal or external sources in the Databricks Lakehouse. The Databricks Lakehouse Platform combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. This unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science, and machine learning. With Databricks notebooks, developers and analytics teams can collaboratively write code in Python, R, Scala, and SQL, plus explore data with interactive visualizations and discover new insights. You can confidently and securely share code with co-authoring, commenting, automatic versioning, Git integrations, and role-based access controls. As good as MongoDB and Databricks are on their own, together we offer enterprises the unmatched ability to work with live application data across traditionally separate domains. This ability allows your teams to deliver what we call application-driven analytics . How does this work? Using MongoDB and Databricks together MongoDB and Databricks offer several ways to integrate the two systems, but the primary means is MongoDB’s Spark Connector. The Spark connector can be used within Databricks notebooks to directly query live application data managed in MongoDB collections and then loaded into data frames for further processing. You can also transform and/or enrich this data with data ingested from other sources using SparkSQL. Queries issued by the Spark Connector can be pushed down to MongoDB's aggregation framework and indexes for pre-processing, significantly improving query efficiency (measured in milliseconds not seconds or minutes). Result sets generated from the Databricks notebooks can then be inserted back into MongoDB collections or can be pushed into Delta Lake for long-running analytics and machine learning. Easier integration using Databricks' MongoDB Notebook A Databricks notebook is a web-based interface that contains runnable code, visualizations, and explanatory text in the form of paragraphs. It lets personas, such as data scientists and data engineers, build linked sets of code in different languages and visualize results in a format in which they are used to working. Notebooks are great for collaboration and can be easily iterated on and improved. MongoDB and Databricks created an example notebook that has sample code for: Reading the data from MongoDB Atlas collections as is into Spark dataframes. Pre-processing and filtering the data from Atlas collections using the aggregation framework, before passing into Spark dataframes. Enriching/transforming the data using SparkSQL Writing the enriched data back to the MongoDB Atlas collection. Figure 1: Screenshot of data sources in a Databricks notebook. This notebook can help as an initial template for developers to start building complex transformation jobs on MongoDB data with Databricks platform. Interested in a practical example of how this works? Let's demonstrate how you can run analytics on a sample sales dataset using MongoDB's aggregation framework and visualize it with Charts. The example also explains how you can enrich this data using our Databricks notebook and load that back to MongoDB. Refer to the GitHub repo for the same. Figure 2: Ways to integrate MongoDB and the Databricks Lakehouse Platform. In addition to Spark, MongoDB and Databricks provide seamless integration through shared Cloud Object stores to enable a more traditional data exchange using analytics-optimized formats such as Parquet, as well as event streaming integration using Apache Kafka. Together, MongoDB and Databricks offer unparalleled abilities to unify and process data from disparate systems in real-time. And now with the newly announced Databricks notebooks integration, data engineers and data scientists have an even easier and more intuitive interface to harness MongoDB data for their most sophisticated analytics and AI processing, making real-time applications more intelligent. Conclusion MongoDB Atlas along with Databricks Platform together will help organizations handle the increasing convergence between operational and analytical workloads. This convergence enables application-driven analytics and will help you build smarter applications and derive the right insights in real-time. Reach out to firstname.lastname@example.org to learn more.
MongoDB and Google Partnership Gains Momentum
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.
Building Together: MongoDB Is Google Cloud’s Technology Partner of the Year for Data Management
Few companies can credibly claim to understand and work with data as effectively as Google, so when Google Cloud announced this week that MongoDB is the Google Cloud Technology Partner of the Year for Data Management, the award felt particularly meaningful. But if the honor was just a matter of two leaders in data management slapping each other on the back, it wouldn't be that interesting. No, what makes the award compelling is the customer success that Google Cloud and MongoDB have jointly enabled. We agree with Google Cloud's vision that "True transformation spans the entire business and enables every person to transform," and together have helped customers to achieve this. Some customers elect to run MongoDB Atlas, our fully managed database service, on Google Cloud because of its broad footprint (Atlas is available in 29 Google Cloud regions), cost/performance benefits, seamless security and scalability, and the recently launched pay-as-you-go option that simplifies subscriptions and can reduce costs. Others have gone further, choosing to take advantage of combining the best services from both companies, like using Google Kubernetes Engine (GKE) or Google Cloud Run for their application tier and MongoDB for their data tier. And some choose Google Cloud so they can tie into Google services that are tightly integrated with MongoDB, including BigQuery, Datastream, and Dataproc. A few examples that show the ambition and innovation MongoDB and Google Cloud customers are bringing to their products: Precognitive combines device intelligence, advanced behavioral analytics, and a real-time decision engine to accurately detect and prevent fraud for online businesses. Using Google Cloud Bigtable as their data store for behavioral and device data, and MongoDB as the data store for everything else, Precognitive is able to capture and analyze vast amounts of data from around the world, in near real time, to combat fraud. Read our full story on how Precognitive uses MongoDB Atlas . Forbes , the world's largest business media brand, reaches more than 140 million people worldwide every month across a number of online and offline channels. The company needed to innovate its way through the global COVID-19 pandemic and turned to MongoDB Atlas running on Google Cloud to enable dramatically better agility. Among other initiatives, the publishing giant married MongoDB's flexible data schema with Google Cloud’s machine learning services to deliver a trending story recommendation engine for journalists. Read our full story on Forbes’ migration to Atlas — and the successes of that move . One of the things we love about working with Google Cloud is the company's pragmatic approach to solving customer problems. Customers tend to choose a predominant cloud vendor upon which to build the majority of their applications, and Google Cloud often serves this role. At Plaid , which helps retailers decipher the complexities of consumer behavior, the company chose to migrate its legacy databases to MongoDB Atlas running on Google Cloud, allowing it to tap into Google Kubernetes Engine, Google Cloud Engine (GCE), Cloud BigTable, and BigQuery. But Plaid also needed to ensure it could run across multiple clouds, which Google Cloud enables with its Anthos service . MongoDB and Google Cloud, working together, deliver that multi-cloud experience for customers. Read our case study on Plaid, MongoDB, and Google Cloud . If you’re ready to experience the fruits of the MongoDB and Google Cloud partnership, take a look at MongoDB Atlas in the Google console . You can get started for free. Read more stories about MongoDB and Google Cloud customers doing great things: How Humanitix uses MongoDB Atlas Device Sync to close the education gap, one great app experience at a time Powering France’s Yellow Pages: How Solocal Turned to MongoDB Atlas and Google Cloud to Manage Two Billion Visits a Year How Macquarie Bank Built a Real-Time Payments Platform in Weeks KODE Labs: Advanced smart building management delivers on promise of sustainable buildings
MongoDB and AWS Expand Global Collaboration
MongoDB launched as a developer-friendly, open source database in 2009, but it wasn't until 2016, when we released MongoDB Atlas , our fully managed database service, that the full vision for MongoDB truly emerged. Realizing that vision, however, has never been a solo effort. From the earliest days, MongoDB has partnered with a range of companies, but none more closely than with Amazon Web Services (AWS) as we've joined forces to make the developer experience as seamless as possible. Now we're kicking that partnership into overdrive. As announced today , MongoDB is expanding our global partnership with AWS. Though details of the agreement are confidential, the results will not be: Customers stand to benefit from deeper, broader technical integrations, improvements in migrating workloads from legacy data infrastructure to modern MongoDB Atlas, and more. For those of us who have worked to grow this partnership, it's exciting (and rewarding!) to see the scope of the work envisioned by MongoDB and AWS, together. On that note, it's worth revisiting how we got here. Building together From the earliest days , we've positioned MongoDB as the best way to manage a wide variety of data types and sources, in real time, at significant scale. Back then we called it "Big Data," but now we recognize it for what it is: what all modern data looks like. Then and now, MongoDB came with an open license that encouraged developers to easily access and tune the database to their needs. And so they did, with many developers opting to run their instances of MongoDB on AWS, removing the need to buy and provision servers. In fact, almost from the start of the company, we have worked closely with AWS to ensure that MongoDB users and customers would have an excellent experience running MongoDB on AWS. It was a great start, but it wasn't enough. Developers, after all, still had to fiddle with the dials and knobs of managing the database. This began to change in 2011, when the company released the MongoDB Monitoring Service (MMS). MMS made it much easier to monitor MongoDB clusters of any size. By 2013, we rolled MMS, Backup, and other MongoDB services into the MongoDB Management Service, and continued to work closely with AWS to optimize these services for MongoDB customers. Then in 2016, again with extensive AWS assistance, we launched MongoDB Atlas, a fully managed, integrated suite of cloud database and data services to accelerate and simplify how developers build with data. Making life easier for developers was the vision that co-founders Dwight Merriman and Eliot Horowitz had when they started MongoDB (then 10gen) in 2007. That vision has always depended on a strong partnership with AWS. This partnership got even stronger, as we just announced , with the promise of even better serverless options, expanded use of AWS Graviton instances to improve performance, and improved hybrid options through AWS Outposts. Beyond product, we'll also be more closely collaborating to reach and educate customers through joint Developer Relations initiatives, programs to reach new customers, and more. As good as our partnership has been, it just got significantly better. Although focusing on how the two companies compete may be convenient (for example, both organizations provide database services), how we cooperate is a more compelling story. So let's talk about that. A mutual obsession Over the past 15 years, MongoDB has built an extensive partner ecosystem. From open source mainstays like Confluent, to application development innovators like Vercel, data intelligence pioneers like BigID, and trusted system integration powerhouses like Accenture, we work closely with the best partners to ensure developers enjoy an exceptional experience working with MongoDB. As already noted, AWS is the partner with which we've worked most closely for the longest time. That partnership has resulted in tight integration between MongoDB and AWS services such as AWS Wavelength, Amazon Kinesis Data Firehose, Amazon EventBridge, AWS PrivateLink, AWS App Runner, Amazon Managed Grafana, and more. We also recently announced Pay as You Go Atlas on AWS Marketplace , giving customers even more options for how they run MongoDB on AWS. Additionally, as part of our new strategic agreement, we'll be offering joint customer incentive programs to make it even easier for customers to run proofs of concept and migrate from expensive legacy data infrastructure to MongoDB Atlas running on AWS. If this seems to paint an overly rosy picture of our partnership with AWS, it's worth remembering that the guiding principle for both AWS and MongoDB is customer obsession. Of course we've had moments when we've disagreed over how best to take care of customers, because every partnership has its fair share of friction. But behind the scenes, our product, marketing, and sales teams have worked together for years to meet customer needs. Customers seem to recognize this. In MongoDB's most recent earnings call, we announced that we now have more than 33,000 customers — including Shutterfly , Cox Automotive , Pitney Bowes , and Nesto Software — many of which choose to run Atlas on AWS. Still not convinced? There's perhaps no better way to understand what MongoDB can do for your organization than to try it. You can try Atlas for free , or you can choose to pay-as-you-go by starting with Atlas on the AWS Marketplace . Either way, we hope you'll let us know what you think.
MongoDB is One of Battery Ventures' 25 Highest-Rated Public Cloud Computing Companies to Work For
Crain's recently recognized MongoDB as one of the best places to work in New York City. Today, Battery Ventures announced that MongoDB is also one of the best places to work in the cloud; specifically, Battery named us one of the " 25 Highest-Rated Public Cloud Computing Companies to Work For ." Battery compiles the list based on Glassdoor ratings and reviews left by employees. In other words, MongoDB's inclusion in the recognition depends upon current and past employees rating MongoDB highly. This makes sense to me, as I fit into both camps. I worked for MongoDB from 2013 to 2014, and loved it. I recently returned, and continue to find it the best place I've ever worked. Apparently I'm not alone in loving MongoDB. Indeed, in addition to this most recent honor from Battery, MongoDB also ranks high on Inc.'s " best led" and "best workplaces " lists, not to mention BuiltIn's " 100 Best Large Companies to Work For ." Why do people love working for MongoDB? For me, it's a combination of great people and great products. When I joined MongoDB back in 2013, it was because of its fresh, open approach to data. MongoDB was so approachable, so easy to use. Developers adored it and quickly became productive with it, making MongoDB one of the most popular databases on the planet. Since that time, MongoDB has added things like full-text search, data visualization, and more, making it the industry's leading data platform. Which is cool, but incomplete. As much as I love to work for a market leader, it's the people of MongoDB that make it a near-perfect employer. Many of the people I loved to work with back in 2013 are still here, and they've been joined by other outstanding, humble people. MongoDB really is the perfect confluence of great technology and great people. Here is what a few of my MongoDB colleagues shared as to their reasons for working here. Annie Dane, Strategic Account Marketing MongoDB is an incredible place to build your career with a tremendous amount of support to do so, including a Learning and Development team that provides a multitude of training opportunities. Additionally, people at MongoDB really care about each other: we encourage a healthy work/life balance and new parents (and their babies) are very welcome at MongoDB, as evidenced during Covid. Mat Keep, Product Marketing Every organization’s success is now defined by software, and that software’s success is defined by data. MongoDB eliminates many constraints developers have faced working with data, which makes it such an exciting place to work as I get to help customers build new applications and modernize existing ones. At MongoDB we get to help address some of today's toughest challenges and most interesting initiatives shaping our world. Angie Byron, Community Management MongoDB is filled with humble, wicked-smart people who make a concerted effort to lift each other up. These traits hold true across departments, across org chart levels, and across levels of technical depth. Additionally, as a queer person, I've never been part of a company that takes diversity and inclusion so seriously and backs it up with real action. Just in the last few months, we've had a panel to talk together about our coming out experiences, trans-specific programming with amazing guest speakers, and more. At MongoDB, we are passionate about our mission of freeing the genius within everyone by making data stunningly easy to work with. We'd love to have you be part of our team. Interested in joining MongoDB? We have several open roles on our teams across the globe and would love for you to transform your career with us!
How Medtronic Manages Machine Data in MongoDB
While many think Big Data is all about “big,” the reality for most organizations is that data variety is a far thornier problem to tackle. Just ask Medtronic . Medical equipment maker Medtronic, perhaps best known for its pacemakers, offers devices and therapies that address more than 30 diseases. Last year the company served 9 million patients and this year the company announced that it serves a patient in some way every three seconds. In addition, Medtronic collects more than 30 million data samples about its devices every day. Matthew Chimento, principal test engineer and project manager at Medtronic, notes that more than 150 data collection and processing steps have been added to Medtronic’s manufacturing process in the last three years, and 40% of all of Medtronic’s stored data has been collected in the last two. Humans aren’t great at collecting data, but machines are, and “we have a lot of machines.” Now if only those machines all spoke the same language. Data Variety: Problem And Opportunity Unfortunately, with a proliferation of machines comes a proliferation of different data types. And while the media likes to talk about “Big Data” as if it were all about volume, companies like Medtronic realize Big Data is primarily a matter of data variety, as a NewVantage Partners survey discovered: Furthermore, for regulatory reasons, Medtronic must save device data for 10 years after the last implant of the device. Since those devices can last 20 years, some data is 30 years old, which means that Medtronic must contend with information spread across a multitude of obscure database systems, in a wide variety of formats. Does Your Data Speak MongoDB? To manage this data complexity, Medtronic turned to MongoDB. About two years ago, Chimento’s colleague, Jeff Lemmerman, a senior software engineer at Medtronic, heard about MongoDB. Intrigued by the NoSQL database and its potential to help Medtronic tame its ever-changing data requirements, Lemmerman launched a proof of concept, which “basically consisted of choosing one battery model that we manufacture.” When the battery goes through electrolyte fill – a step in the manufacturing process – all of the component data is loaded into MongoDB. “This is a very simple place to start,” he said. Lemmerman has high hopes for the next steps with MongoDB. He hopes to begin loading manufacturing data on every component Medtronic makes directly into MongoDB, and aggregating that data into a device-level view, and MongoDB’s data model will make that easy. “You’re trying to facilitate analysis across components, and you really want simple, fast queries … instead of doing those nasty joins that we saw in my relational example, I’m able to find the complete history for a component with a very simple query.” What Can MongoDB Do For You? Like Medtronic, your data changes constantly as business requirements change. And, like Medtronic and most enterprises, you likely use a relational database to manage that data. For reasons noted above, as well as here , an RDBMS is a poor fit for data that changes often or for applications that need to scale. We therefore invite you to check out the RDBMS to MongoDB Migration Guide to determine how best to migrate data from your RDBMS to MongoDB.
MongoDB And Teradata Join Forces To Make Big Data Smart
As enterprises increasingly depend on MongoDB to build and run modern applications, they need high-quality analytics solutions to match MongoDB's powerful data model. With the partnership Teradata and MongoDB just announced , they just got one. And it's exceptionally cool. With data analytics leader Teradata we've built a bi-directional connector that gives organizations interactive data processing at extremely fast speeds. Teradata's bi-directional QueryGrid connector allows Teradata customers to integrate massive volumes of JSON with cross-organizational data in the data warehouse for high performance analytics. Through the connector, MongoDB customers will have access to JSON that has been enriched by Teradata to support rapidly evolving applications for mobile, Internet of Things, eCommerce, social media and other applications. In other words, users will soon be able to easily connect MongoDB applications and analytics running on Teradata. The Future Is JSON For the past 40 years, enterprises have stored their data in the tidy-but-rigid tables and joins of relational databases. Given the explosion of unstructured data, however, enterprises need a more expressive, flexible way of describing and storing data. Enter JSON. MongoDB stores data in JSON documents, which we serialize to BSON . JSON provides a rich data model that seamlessly maps to native programming language types, and the dynamic schema makes it easier to evolve one's data model than with a system that enforces schemas like a relational database (RDBMS). Marrying MongoDB's operational database with Teradata's analytics platform a great way to bring together all of an enterprise's data. A Virtuous Cycle One way of thinking about the interaction between MongoDB and Teradata is to picture a crowd of people. MongoDB interacts with individuals within the crowd in real-time while Teradata looks for patterns within the crowd. With this connector, organizations can push their MongoDB data (website clicks, purchases, etc.) into Teradata, which runs queries against the data, looking for patterns. This intelligence is then pushed back to MongoDB, enriching the interaction with individual eCommerce buyers, mobile users, etc. It's a virtuous cycle, as Teradata describes on its blog . Here's what this looks like for an eCommerce application: By bringing the two together, an eCommerce vendor's interactions with its customers will continuously improve as their MongoDB-based application gets smarter and more tailored by Teradata analytics. Importantly, for enterprises that expect to use both relational databases and MongoDB, Teradata's JSON integration unifies relational and MongoDB data analysis. And, Not Or This last point is worth repeating. As much as enterprises might wish to shed their IT investments and start over, the reality is that they can't and won't, as a 2012 Gartner analysis found: By giving organizations an easy way to connect MongoDB's operational data with Teradata's enterprise data warehouse, the two organizations ensure existing and new data sources can coexist. By working closely together, MongoDB and Teradata give enterprises the best of a modern, operational database with a powerful analytics platform.
You Know What's Cool? 1 Trillion Is Cool
A million used to be cool. Then Facebook upped the ante to one billion. But in our world of Big Data, even a billion is no longer the upper end of scale, or cool. As I learned last night, at least one MongoDB customer now stores over 1 trillion documents in MongoDB. 1 trillion . That's cool. It's also far bigger than any other database deployment I've seen from any NoSQL or relational database, even from the simple key-value or columnar data stores that are only programmed to handle simple workloads, but to scale them well. That's what makes MongoDB über cool: not only does it offer dramatic, superior scale , but it does so while also giving organizations the ability to build complex applications. MongoDB delivers the optimal balance between functionality and performance, as this illustrates: Many systems are focused on nothing more than storing your data, and letting you access it quickly, but one and only one way. This simply isn’t enough . A truly modern database must support rich queries, indexing, analysis, aggregation, geospatial access and search across multi-structured, rapidly changing data sets in real time. The database must not trap your data and hinder its use. It must unleash your data . All 1 trillion documents of it. Want to see how major Global 2000 organizations like Bosch, U.S. Department of Veterans Affairs, Genentech, Facebook and many others scale with MongoDB? Easy. Just register to attend MongoDB World, June 24-25 in New York City. You can use my discount code to get 25% off: 25MattAsay.
Beyond NoSQL: A Modern Database Manifesto
There is no such thing as NoSQL. Not as we tend to think of it, anyway. While NoSQL was born as a movement away from rigid relational data models so web giants could embrace Big Data with scale-out architectures, the term has come to categorize a set of databases that are more different than they are the same. This broad categorization doesn’t work. It’s not helpful. While we at MongoDB still sometimes refer to NoSQL, we try to do it sparingly, given its propensity to confuse rather than enlighten. Deconstructing NoSQL Today the NoSQL category includes a cacophony of over 100 document, key-value, wide-column and graph databases . Each of these database types comes with its own strengths and limits. Each differs markedly from the others, with disparate models and capabilities relative to data storage, querying, consistency, scalability and high availability. Comparing a document database to a key-value store, for example, is like comparing a smartphone to a beeper. A beeper is exceptionally useful for getting a simple message from Point A to Point B. It’s fast. It’s reliable. But it’s nowhere near as functional as a smartphone, which can quickly and reliably transmit messages, but can also do so much more. Both are useful, but the smartphone fits a far broader range of applications than the more limited beeper. As such, organizations searching for a database to tackle Gartner’s three V’s of Big Data -- volume, velocity and variety -- won’t find an immediate answer in “NoSQL.” Instead, they need to probe deeper for a modern database that can handle all of their Big Data application requirements. Modern Databases For Modern Data One of these requirements is, of course, the ability to handle large volumes of data, the original impetus behind the NoSQL movement. But the ability to handle volume, or scale, is something all databases categorized as “NoSQL” share. MongoDB, for example, counts among its users those who regularly store petabytes of data, perform over 1,000,000 operations per second and clusters that exceed 1,000 nodes. A modern database, however, must do more than scale. Scalability is table stakes. It also must enable agility to accelerate development and time to market. It must allow organizations to iterate as they embrace new business requirements. And a modern database must, above all, enable enterprises to take advantage of rapidly growing data variety. Indeed the “greatest challenge and opportunity” for enterprises, as Forrester notes, is managing a “variety of data sources,” including data types and sources that may not even exist today. In general, all so-called NoSQL databases are much more helpful than relational databases at storing a wide variety of data types and sources, including mobile device, geospatial, social and sensor data. But the hallmark of a modern database its ability to allow organizations to do useful things with their data. Defining The Modern Database To count as a modern database, then, a database must meet three requirements. While relational databases are able to manage some of these requirements, and newer so-called “NoSQL” key-value or wide column data stores meet others, only MongoDB meets all three requirements. The database MUST scale . As data volume and velocity grows, so the database must grow too. It should scale horizontally and elegantly, without doing unnatural things to your application, in the cloud or on commodity hardware. Meeting the base requirements -- like having enough capacity to serve your customers -- should be a given. The database MUST adapt to change . The speed of business accelerates and your database must keep pace, enabling iteration. This means you must be able to process and mine new data sources and data types without the database breaking a sweat (or you breaking your back or budget). Your schema must flow from your application requirements, rather than forcing your application to fit a predefined, rigid schema. The database MUST unleash your data . Just storing data isn’t enough. You must be able to exploit the data, which particularly means you must be able to ask significant questions of your data. In part this means that the database must support rich queries, indexing, aggregation and search across multi-structured, rapidly changing data sets in real time. But it also means that it must support data for modern use cases including mobile, social, Internet of Things and other systems of engagement. Some relational databases can handle a few of these requirements, yet fail in the essential need to deliver scale and adaptability. Some newer databases, including so-called “NoSQL” key-value or wide column data stores, meet still other requirements, yet don’t give organizations the latitude to unleash their data. In fact, they constrain you to look up data by the key with which it was written unless you integrate external search engines and analytics nodes, which can create other problems. MongoDB: A Modern Database For Today's Business Needs But only one database today can deliver on each of these critical components of a modern database. Only one database offers orders of magnitude more productivity for developers and operations teams alike, while still delivering petabyte scale and lightning-fast performance. Only MongoDB, the modern database that tens of thousands of organizations depend upon to build and run today’s most demanding applications. To learn more about how MongoDB has enabled some of the world’s largest and most innovative companies to deliver applications and outcomes that were previously impossible, download our new whitepaper .
Looking beyond labels like relational and NoSQL
According to a new Dice.com salary survey , MongoDB ranks as one of top-10 most highly compensated technology skills. Indeed.com rates MongoDB as the second hottest job trend. And DB-Engines.com, which ranks over 200 databases on their relative popularity, MongoDB is now the fifth-most popular database in the world, this month surpassing IBM's DB2. All great, right? Maybe. Buried in the Dice.com data, as well as the Indeed.com data, is evidence of real confusion. For example, of the top-10 most highly compensated skills in Dice.com's survey is "NoSQL ." NoSQL is not a technology. It's not really something a developer can "know" in any real sense. NoSQL is a movement that describes a different way of modeling data but, as Basho founder Justin Sheehy correctly noted , there are as many differences among so-called NoSQL databases as there are similarities. As such, knowing Basho's Riak won't really help you understand MongoDB. Perhaps at a high, conceptual level, but expertise in one doesn't really translate into familiarity with another. They are different databases with different approaches. Employers looking for generic NoSQL skills need to think more deeply about what their application requirements are. Looking beyond relational databases for modern application requirements is a good start, but looking to generic "NoSQL" is not sufficient. Organizations should be looking for a modern database that dramatically improves developer productivity, encourages application iteration and enables a new wave of transformational applications in areas like Big Data , Internet of Things , mobile and more . That database is MongoDB. Is MongoDB "NoSQL." Sure. But it's much bigger than that ( based on what people search for on Google , many organizations already seem to understand this). MongoDB is the fastest-growing database in the world , not because it fits the NoSQL category, but because it significantly improves the productivity of developers and the organizations for which they work. So if you're looking to hire technology talent, you're far more likely to be successful hiring an experienced MongoDB engineer than a "NoSQL engineer." MongoDB, after all, is an actual database. NoSQL simply describes an important movement.