Today, Forrester released The Forrester Wave™: Document Stores, Q3 2016, recognizing MongoDB as a Leader based on our current offering, strategy, and market presence. The report said that "MongoDB is one of the most popular document stores."
As you may recall, a few weeks ago Forrester published another important piece of research on databases, The Forrester Wave™: Big Data NoSQL, Q3 2016. In that report we were also acknowledged as a leader, with a “5 out of 5” score in 19 of the 26 criteria.
In this latest report, Forrester evaluates the document database capabilities of a range of database technologies, from MongoDB to traditional relational databases. The very existence of such a report is remarkable, but beyond our position as a Leader, I see in this report evidence that consensus has come to endorse our vision of what the world needs in a datastore.
The first release of MongoDB was just over 7 years ago. One of our underlying beliefs was that the document data model is the right way to model data, for a number of reasons. Documents are more flexible and inherently more agile than the relational model; they map to the objects of modern programming models; they are easier and more natural for developers to reason about; and, it turns out, large volumes of documents are much easier to scale to meet the needs of cloud infrastructure and modern workloads.
Back in 2009, traditional relational vendors did not hold the same convictions of the importance of the document model. But now, just a few years later, virtually every mainstream database supports the document data model. The reason is clear - the market has embraced the document model, and vendors have either joined the document revolution, or they’re getting left behind.
The world is ready for a document database to be its default. 61% of the enterprises surveyed by Forrester for the Big Data NoSQL Wave are using, planning to use, expanding or upgrading to NoSQL over the next 12 months, and we are confident that MongoDB will continue to be the most popular choice.
I believe that Forrester’s research makes a critical point - not all document databases are created equal. We developed MongoDB with a broad range of use cases in mind, which is why it excels at so many workloads. Our document model is a superset of other data models, including key-value, graph, object, and relational, and we natively support complex manipulations on these data with operators like $lookup and our new graph operators in 3.4.
But it’s not just the data model that makes MongoDB unique. Modern applications require flexible approaches to “always on” global deployments, and easy ways to meet demanding SLAs. Our replication and sharding architecture, pluggable storage engine framework, and tunable consistency mean that an entire spectrum of data semantics can be achieved through configuration, rather than by mixing and matching from a grab-bag of different database products.
Another central aspect of our vision is that embracing the flexibility of the document data model does not require sacrificing the ability to safeguard data integrity. While this may be true with most document stores, including relational databases, with MongoDB this is not the case at all. MongoDB’s document validation features allow you to be incredibly strict in how you enforce your schema, from just a few fields, to every field in your model, to no validation at all. Best of all, we don’t require you learn a new language to express schema; instead, we rely on the find() syntax that every MongoDB developer and DBA knows today, which also means we can take advantage of Boolean, geospatial, data typing, wildcard expressions and more - it’s incredibly powerful.
Our tools and integrations for MongoDB meet the needs of a broad range of enterprise users. From our beautiful GUI for the database, MongoDB Compass, to our powerful Connector for BI which provides SQL access for analysis, to our management tools like Ops Manager and Cloud Manager, which provide a comprehensive suite of monitoring, automation, and backup and point-in-time recovery capabilities - we’ve got you covered. We're also innovating in the next generation of analytics, machine learning, and streaming with our new MongoDB Connector for Apache Spark.
To summarize, our vision for the modern datastore incorporates the flexibility and power of the document model, handles high availability and scale out as core features, retains the ability to safeguard data integrity, and affords enterprises the ability to leverage an ecosystem of analytical tools, and one last thing... it is a first-class citizen of the cloud. This is why we created our database as a service, MongoDB Atlas: the simplest, most robust, and most cost effective way to run MongoDB in the cloud. Using MongoDB Atlas, enterprises can spin up a fully managed, monitored, and backed up cluster with the click of a button, in just a few minutes. Now, regardless of what type of infrastructure an enterprise wants to run, they have the flexibility to deploy and manage MongoDB with ease.
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.