How MongoDB Changed Database Management and the Developer Lifecycle for Higher Scale and Performance

TL; DR: MongoDB offers a developer data platform that uses a flexible document data model that can handle any type of data at scale. Tens of thousands of customers and millions of developers leverage the MongoDB platform to satisfy their most demanding use cases and can build new applications in their preferred programming language. Released in 2016, MongoDB Atlas takes MongoDB’s capabilities a step further by offering a fully managed database service — available across Amazon Web Services (AWS), Azure, and Google Cloud — along with providing an integrated suite of tools to streamline developer productivity.

2007 was a significant year for technological innovation and advancement, during which a fundamental shift in modern computing occurred. The move to cloud services was underway, and companies began to push the envelope on how society’s relationship with the internet and technology evolved going forward. The world was becoming more connected. Twitter and Facebook went global, Steve Jobs introduced the first iPhone, and Amazon had just launched AWS. Life was never the same again because of this massive shift to new types of technology.

During this time, Dwight Merriman, Eliot Horowitz, and Kevin Ryan —veterans of the online advertising company DoubleClick that Google acquired — were quickly learning the limitations of managing huge volumes of data with traditional relational databases when scaling the company to serve more than 400,000 ads per second. Based on their experience, they believed they could create a more scalable and flexible solution for storing and managing data at high volume and velocity to help developers become more productive and improve business operations.

The result was the founding of the company now known as MongoDB (formerly 10gen) in 2007. Merriman and Horowitz saw an opportunity to use this new cloud-computing model with virtually unlimited scalability to build a developer-centric platform. Their breakthrough came when they realized that a document-based data model with an open development approach could disrupt the massive database market. This innovation gave developers a better way to manage and interact with data in a natural and flexible way to meet every type of workload demand—enabling them to quickly build their next big idea without the constraints of legacy databases.

MongoDB started as a free, open-source database, allowing users to download it from the web or a repository. But what distinguishes MongoDB is its foundational pillars: a document data model, distributed storage, and a consistent developer experience. With its software, developers can have the same great experience using MongoDB.

MongoDB changed the way organizations approached database management forever. It helped make developers more productive and able to get software to market quicker while their applications were inherently more resilient and scalable due to its distributed architecture. MongoDB also led the way for NoSQL databases and created a massive wave of developer adoption. And it all starts with its foundational document data model.

“About 80 to 90 percent of the world’s data is unstructured, and the amount of data being generated continues to grow exponentially,” said Ben Flast, Lead Product Management at MongoDB. “If you have an inflexible data model that can’t adapt, scale, or handle all types of data, it’s like a hardware decision — near-instantly out of date. Relying on a rigid data model can essentially ‘freeze in time’ how an enterprise thinks about and works with its data.”

Because of all this, organizations continue to realize they need a more flexible model for incorporating new technologies into applications across an enterprise, quickly adapting to dynamic market changes, or continuously inventing new experiences for end-users. And they want to do this easily, using a single platform instead of a cobbled-together tech stack with various tools bolted onto an already-complex web of databases, code libraries, and APIs to get to the level of functionality they need.

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MongoDB allows developers to use their programming language of choice. Its query API sets up this advantage, simplifying workloads and enabling developers to work outside a singular programming environment. Users don’t have to learn new languages and can use anything from Python to Java to Rust.

In 2016, MongoDB released Atlas, and its business model shifted from selling software licenses to being a Software-as-a-Service (SaaS) provider. Since then, the company has seen massive growth and adoption. MongoDB had already offered two versions of its software—a free Community edition and an Enterprise Advanced version—both of which are self-managed. MongoDB Atlas, however, is a fully managed cloud service available on AWS, Google Cloud, and Microsoft Azure in more than 110 geographic regions.

Atlas handles all cloud-resource provisioning with auto-scaling capabilities and provides all the tools needed to build modern applications end-users love. Atlas can run virtually any type of workload across an entire enterprise—it’s one platform for many workloads.

“MongoDB Atlas adoption continues to grow from current customers putting more and more workloads on it to new customers adopting it as well,” said Flast. “By offloading the undifferentiated work associated with managing databases, Atlas allows software development teams to focus on building differentiating features and high-value tasks instead of dealing with the operational muck of infrastructure management.”

An example of Atlas’s success is with one of its biggest customers, Toyota Financial Services (TFS) — a subsidiary of Toyota Motor Corporation — that operates in more than 40 countries, provides auto loans, leases, and insurance to prospective Toyota owners, and helps dealers finance expansion.

TFS observed that the transportation sector is rapidly changing with end-users’ desire for the finance processes to be faster, more mobile, and more personalized. To accommodate these new consumer expectations, TFS realized it needed to digitally transform itself and empower its development teams. One key reason for adopting Atlas was that TFS discovered its database team was spending 40% of its time managing infrastructure, patching, and supporting the platform. Adopting Atlas, which provides the backbone for the company’s data fulfillment service, meant TFS could also utilize Atlas Search and Atlas Charts and stop using additional tooling.

Over the years, MongoDB Atlas has grown to become a robust developer solution because of MongoDB’s dedication to solving software development challenges. For example, MongoDB added Atlas Search to the platform to simplify use cases requiring full-text search functionality. Traditionally for software developers to build search functionality into their applications, they needed to stand up an additional database optimized for search alongside their operational database.

This leads to more complexity for the developer as it increases the operational overhead with another database to provision, secure, upgrade, patch, back up, monitor, and scale. Additionally, it contributes to unnecessary architectural complexity with the added difficulty of keeping data in sync between two separate systems. By integrating the database, search engine, and sync mechanism into a single, unified, and fully managed platform, Atlas Search enables developers to build relevance-based search capabilities directly into their applications using a single platform.

Helvetia, one of the largest Swiss insurance companies serving more than 7 million personal and corporate customers across Europe, recently migrated off their transactional and search databases, consolidating their application architecture by utilizing MongoDB Atlas and Atlas Search. Helvetia observed that its feature releases now only take three hours, a time-savings of 90% compared to their previous solutions.

MongoDB traditionally released its new features and enhancements in a single release annually, but in 2021 moved to a more frequent delivery model with new Rapid Releases pushed live once per quarter. Many of the updates featured in 2021’s Rapid Releases involved enhancing Atlas’s times series capabilities. MongoDB’s document data model has always been a natural fit for time series data, but MongoDB wanted to make its capabilities more intuitive and easier to use natively to set up queries and time series applications without additional configuration. So it added that functionality to its database. That’s why the company adds new features to answer overall developer needs. Users don’t have to seek out additional database options and then learn another query language and API, all while trying to manage, transform, and sync their data between multiple disparate systems. With MongoDB, organizations can simplify their application architecture and keep the majority of their workloads under one roof.

“Applications considered modern today will soon be considered legacy because of the current pace of innovation and the expectations end-users will have when it comes to interacting with an application,” said Flast. “Just look at chatbots and search right now, and how with new plugins, consumers are able to interact with applications in real-time to book travel, get restaurant recommendations and make reservations, or easily understand what’s in a PDF or a company’s internal knowledge repository by just asking questions or telling an application what they need it to do in a natural way. And we’re only at the beginning of this shift in technology. This is why we’re continuously innovating with Atlas to provide our customers with new capabilities that are essential for organizations both today, and necessary to usher in the next technological shift in software we’re seeing.”