MongoDB Named a Leader in 2022 Gartner® Magic Quadrant™ for Cloud Database Management Systems

Vivek Bhalla
January 10, 2023
#Cloud

MongoDB is proud to be named a Leader in the 2022 Gartner® Magic Quadrant for Cloud Database Management Systems (DBMS). We believe this achievement makes MongoDB the only pure-play application database provider recognized as a Leader.

Here at MongoDB, we feel the true achievement is not in the placement, rather the means by which it was achieved. At MongoDB we strive to offer engineering teams an enhanced approach through a unified developer data platform as opposed to contending with multiple disparate and fragmented technologies and products. Our desire is to give customers a single cohesive design philosophy with a consistent workflow, API, data model, query language, management and navigation through this unified data developer platform. This design is flexible enough to service almost any application - so significantly reduces cognitive toil engineering teams have to deal with. Therefore, teams can build and release faster, evolving their apps at higher velocity than competitors. We believe that this philosophy has been validated, not only by the community of developers that have built up around MongoDB, but now by this placement in this publication from Gartner.

Based on our conversations with customers, we don’t think that having a tool for every job makes sense because over time, the tax and cost of learning, managing and supporting those different tools doesn’t make a lot of sense or just becomes cost-prohibitive.1

Dev Ittycheria, MongoDB CEO - November 2022

Cloud Database Management Systems are defined by Gartner as “Core capabilities are that vendors fully supply provider-managed public or private cloud software systems that manage data in cloud storage. Data is stored in a cloud storage tier. Optionally, they may cater to multiple data models and data types — relational, nonrelational (document, key value, wide column, graph), geospatial, time series and others.”

To help users understand this emerging technology landscape, Gartner published its first Cloud Database Management Systems Magic Quadrant back in 2020. Two years on and after an evolving criteria, Gartner has named MongoDB as a Leader in its debut as a qualifying vendor for the latest 2022 Magic Quadrant.

We believe MongoDB was named a Leader in this report due to the R&D investments made in further building out capabilities in MongoDB Atlas, our multi-cloud developer data platform.

These investments were driven by the demands of the developer communities we work with. You told us how you struggle to bring together all of the data infrastructure needed to power modern digital experiences – from transactional databases to analytics processing, full-text search, and edge computing. This is exactly what our developer data platform offers. It provides an elegant, integrated, and fully-managed data architecture accessed via a unified set of APIs.

With MongoDB Atlas, developers are more productive, they ship code faster and improve it more frequently.

Cloud native - First and foremost

Virtually every organization we work with at MongoDB has a cloud strategy. It's not zero sum, so there are some apps that will never go there. However, everyone is either using or looking to use the cloud because of the agility it brings to them. MongoDB delivers this to our customers via a fully managed, cloud-native document-based database; MongoDB Atlas, our globally distributed developer data platform. This platform easily and securely reduces time spent on development cycles and empowers organizations with flexible schema and the tools they need to innovate.

MongoDB Atlas’ multi-cloud clusters take the concept a step further by enabling a single application to use multiple clouds simultaneously. With multi-cloud clusters, data is easily distributed across different public clouds, like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This enables data mobility and resilience without the complexity of manual data replication. This philosophy was taken a stage further earlier this year with the delivery of our serverless architecture. This abstracts away server, storage, and network provisioning, plus the management overhead. Thus, organizations and their development teams can focus on building differentiating features and creating great app experiences for their customers.

Such expanding product vision was called out by Gartner as a notable strength. The authors of the magic quadrant recognised our plans to deliver more comprehensive analytics support and SQL capabilities alongside the existing functionality delivered to date. These additions have extended the document data model to embrace time series, application search and application-driven analytics use cases among others, and deliver an industry proven multi-model general purpose operational and transactional database.

The community of MongoDB users have also spoken on Gartner Peer Insights™; 97% of MongoDB users who provided reviews to Gartner Peer Insights platform said they would recommend us 2 (based on 36 ratings in the last 12 months, as on December 21, 2022).

Customer review from an anonymous user providing MongoDB with a 5 star rating. The review reads as follows: MongoDB is a superb open-source management information system that is completely free. It makes it easier than ever to work with significant quantities of dispersed data. It's an extremely fast, scalable, resilient, transparent, and highly operational database. Querying MongoDB is quite easy and has a very quick learning curve. It is extremely efficient and reads data correctly in real-time. Using this tool, running complex queries has become a breeze, and information processing has become lightning quick. Because of its NoSQL features, it can store data in a Data Source, which allows it to hold information with dynamic attributes. A straightforward terminology is used to write the question.

Evaluating a leader in the Magic Quadrant for cloud database management systems

Gartner evaluated 20 of the most significant cloud DBMS vendors against 15 criteria (7 on execution and 8 on vision). These criteria span current product offering, market responsiveness and record through to innovation and the business model.

Our rapid adoption in the past decade and move to cloud was highlighted by Gartner as a strength. It is our opinion that our placement as a Leader in this Magic Quadrant is an acknowledgement of this.

Visit the Gartner website to obtain the full report here (requires Gartner subscription)3.

Customer momentum

Organizations frequently start their journey with MongoDB by employing it as an operational database. This can be for both, new cloud-native services, as well as modernized legacy apps. Both scenarios are routinely enhanced by appending application search, mobile and analytics use cases to the core operational database requirement. Increasingly more diverse teams are now improving customer experience and attaining business agility by embracing what the MongoDB community have become accustomed to for years. Examples include:

  • Forbes for migration to the cloud in six months

  • Verizon for pioneering 5G connectivity and data storage to the edge

  • Powerledger for legacy data platform migration

Getting started on your Cloud journey

MongoDB Atlas is engineered to help you make the shift to the cloud. We can come in to understand more about your key transformation initiatives and workshop ideas with your teams to accelerate delivery. In the interim, your engineers and developers can familiarize themselves with MongoDB right away by signing up for a free account on MongoDB Atlas. Have them create a free database cluster, load your own data or our sample data sets, and explore what’s possible within the platform.

From November 2022, we have enhanced the MongoDB learning experience. The updated program includes an expanded catalog of courses, streamlined developer certifications, 24/7 exam access, hands-on Atlas labs, and foreign language support. Additionally, the MongoDB Developer Center hosts an array of resources including tutorials, sample code, videos, and documentation.

1Source: SiliconANGLE article, 28th November 2022: “MongoDB’s Dev Ittycheria on how the cloud is expanding developers’ influence”, Mike Wheatley

2 Gartner® and Peer Insights™ are trademarks of Gartner, Inc. and/or its affiliates. All rights reserved. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

3Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. The Gartner logo is a trademark and service mark of Gartner, Inc., and/or its affiliates, and is used herein with permission. All rights reserved.

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Securing Multi-Cloud Applications with MongoDB Atlas

The rise of multi-cloud applications offers more versatility and flexibility for teams and users alike. Developers can leverage the strengths of different cloud providers, such as more availability in certain regions, improved resilience and availability, and more diverse features for use cases such as machine learning or events. As organizations transition to a public, multi-cloud environment, however, they also need to adjust their mindset and workflows — especially where it concerns security. Using multiple cloud providers requires teams to understand different security policies, and take extra steps to avoid potential breaches. In this article, we’ll examine three security challenges associated with multi-cloud applications, and explore how MongoDB Atlas can help you mitigate or reduce the risks posed by these challenges. Challenge 1: More clouds, more procedures, more complexity Security protocols, such as authentication, authorization, and encryption, vary between cloud providers. And, as time goes on, cloud providers will continue to update their features to stay current with the market and remain competitive, adding more potential complications to multi-cloud environments. Although there are broad similarities between AWS, Azure, and GCP, there are also many subtle differences. AWS Identity and Access Management (IAM) is built around root accounts and identities, such as users and roles. Root accounts are basically administrators with unlimited access to resources, services, and billing. Users represent credentials for humans or applications that interact with AWS, whereas roles serve as temporary access permissions that can be assumed by users as needed. In contrast, Azure and GCP use role-based access control (RBAC) and implement it in different ways. Azure Active Directory allows administrators to nest different groups of users within one another, forming a hierarchy of sorts — and making it easier to assign permissions. However, GCP uses roles , which include both preset and customizable permissions (e.g., editor or viewer), and scopes , or permissions that are allotted to a specific identity concerning a certain resource or project. For example, one scope could be a read-only viewer on one project but an editor on another. Given these differences, keeping track of security permissions across various cloud providers can be tricky. As a result, teams may fail to grant access to key clients in a timely manner or accidentally authorize the wrong users, causing delays or even security breaches. Challenge 2: Contributing factors Security doesn’t exist in a vacuum, and some factors (organizational and otherwise) can complicate the work of security teams. For example, time constraints can make it harder to implement or adhere to security policies. Turnover can also create security concerns, including lost knowledge (e.g., a team may lose its AWS expert) or stolen credentials. To avoid the latter, organizations must immediately revoke access privileges for departing employees and promptly grant credentials to incoming ones. However, one study found that 50% of companies took three days or longer to revoke system access for departing employees, while 72% of companies took one week or longer to grant access to new employees. Challenge 3: Misconfigurations and human error According to the Verizon Data Breach Investigations Report , nearly 13% of breaches involved human error — primarily misconfigured cloud storage. Overall, the Verizon team found that the human element (which includes phishing and stolen credentials) was responsible for 82% of security incidents. Because misconfigurations are such common mistakes, they comprise the majority of data breaches. For example, AWS governs permissions and resources through JSON files called policies. However, unless you’re an expert in AWS IAM, it’s hard to understand what a policy might really mean. Figure 1 shows a read-only policy that was accidentally altered to include writes through the addition of a single line of code, thereby inadvertently opening it to the public. That data could be sensitive personally identifiable information (PII); for example, it could be financial data — something that really shouldn’t be modified. Figure 1: Two examples of read-only policies laid out side by side, demonstrating how a single line of code can impact your security. Although the Verizon report concluded that misconfigurations have decreased during the past two years, these mistakes (often AWS S3 buckets improperly configured for public access) have resulted in high-profile leaks worldwide. In one instance, a former AWS engineer created a tool to find and download user data from misconfigured AWS accounts . She gained access to Capital One and more than 100 million customer credentials and credit card applications. The penalties for these vulnerabilities and violations are heavy. For example, the General Data Protection Regulation (GDPR) enacts a penalty of up to four percent of an organization’s worldwide revenue or €20,000,000 — whichever is larger. In the aftermath of the security event, Capital One was fined $80 million by regulators ; other incidents have resulted in fines ranging from $35 million to $700 million . Where does MongoDB Atlas come in? MongoDB Atlas is secure by default, which means minimal configuration is required, and it’s verified by leading global and regional certifications and assurances. These assurances include critical industry standards, such as ISO 27001 for information security, HIPAA for protected healthcare information, PCI-DSS for payment card transactions, and more . By abstracting away the details of policies, roles, and other protocols, Atlas centralizes and simplifies multi-cloud security controls. Atlas provides a regional selection option to control data residency, default virtual private clients (VPCs) for resource isolation, RBAC for fine-tuning access permissions, and more. These tools support security across an entire environment, meaning you can simply configure them as needed, without worrying about the nuances of each cloud provider. Atlas is also compatible with many of the leading security technologies and managers, including Google KMS, Azure Key Vault, or AWS KMS, enabling users to either bring their own keys or to secure their clusters with the software of their choice. Additionally, data is always encrypted in transit and at rest. For example, you can run rich queries on fully encrypted data using Queryable Encryption , which allows you to extract insights without compromising security. Data is only decrypted when the results are returned to the driver — where the key is located — otherwise, encrypted fields will display as randomized ciphertext. One real-world example involves a 2013 data breach at a supermarket chain in the United Kingdom, where a disgruntled employee accessed the personal data of nearly 100,000 employees. If Queryable Encryption had been available and in use at the time, the perpetrator would have downloaded only cipher text. With MongoDB Atlas, securing multi-cloud environments is simple and straightforward. Teams can use a single, streamlined interface to manage their security needs. There is no need to balance different security procedures and structures or keep track of different tools like hyperscalers or key management systems. Enjoy a streamlined, secure multi-cloud experience — sign up for a free MongoDB Atlas cluster today .

January 9, 2023
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024