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Since 2007, MongoDB's document-based database has accumulated millions of users as workload changes to the cloud have accelerated the growth of data collecting overall and the demand for structures to store such data (particularly NoSQL variants like document-based databases). As the most sought-after database (both SQL and NoSQL included) for professional developers to learn internationally during the last four years, according to Stack Overflow, MongoDB doesn't seem to be losing any momentum. As MongoDB's most recent cloud database-as-a-service and data lake products help ensure that MongoDB's rich capabilities evolve to meet new technological needs, I believe that such interest will continue. The database industry is flourishing, expanding dramatically as a result of cloud migrations. Scaling, gathering, and analysing data become simpler once business workloads are in the cloud due to how simple it is to scale data storage in the cloud. Therefore, I anticipate that the amount of data gathered and analytical operations performed on it in the cloud will continue to grow significantly, which will be advantageous for many database providers, especially MongoDB. MongoDB, which I think is the leading document-based database, has a wealth of features, ranging from quick interoperability across various cloud platforms to in-database data transformation. I believe that the majority of the net new data that needs to be kept is primarily for in-depth analysis, necessitating a NoSQL database because of its capacity to store unindexed or unknown categories of data. I believe MongoDB is well positioned to develop rapidly and profit from such scale, with a large amount of opportunity still remaining in converting clients to its cloud database-as-a-service product, Atlas, which accounts for 50% of total revenue. I also think that MongoDB has a loyal following of users and may one day justify a moat. Using a 13% discount rate implying a 9% equity risk premium and a growth rate of 5%, we get a FVE of $304. This also accounts for free cash flow margins hitting 30% by 2032 and 10 year CAGR to be around 28%.

Moat


MongoDB receives a no-moat ranking from me. With the consumers it has so far acquired, I believe the organization benefits from high switching costs. However, as MongoDB lacks profitability and continues to aggressively spend on sales and marketing, it is unclear whether this moat source will result in excess returns on invested capital over the next ten years while the firm is still in its client acquisition phase. Given that I think that data is becoming more and more mission-critical for businesses, I feel that MongoDB benefits from switching costs with its current client base. This makes choosing a database vendor a vital responsibility for a company. Businesses should consider hard before switching databases due to the complexity of company IT infrastructures. Since databases are tied to many different business systems, reintegrating them would take a lot of time and money. Enterprises now more than ever rely heavily on data to serve as the foundation of their operations, both internally and externally through their products. Because of this, how such data is maintained and accessible becomes a crucial business activity. I see switching costs in other enterprise software, such as a steep learning curve in using new data architecture and steep financial and time-related costs associated with "rewiring" how a company stores and accesses data, when I change these storage and access methods of sensitive and highly mission-critical data. These headaches are not dissimilar from switching costs I see in other enterprise software. I believe switching costs are even higher in the world of data architecture compared to other enterprise software because data architectures are much more fundamental and mission crucial to a business than the very enterprise software that accesses the data that is stored. To give an example, consider the catastrophe that would result if the information for weather patterns in Ohio was unintentionally replaced by Weather Channel data for Peru. This may result in severe financial losses for the businesses who use this data, such as airlines or scientific researchers. MongoDB's net yearly recurring revenue, which is based on the client cohort from the prior year period and has consistently been north of 120%, is one way that we can see the stickiness of data structures mirrored in the market. A NoSQL database is MongoDB. This indicates that MongoDB stores data in a different format than a relational, SQL database, which stores data in tables. There are numerous non-relational methods of data storage. However, MongoDB accomplishes this by putting data into collections that are called documents. A few enticing features of document-based formats are their capacity to contain both organised and unstructured data as well as their simplicity in scaling. Although many people believe that MongoDB is the greatest document-based database, there are a number of rivals, including Couchbase and AWS Document DB. We are unable to give MongoDB an extra moat source based on intangible assets due to the presence of these rivals, especially Amazon, who has almost limitless resources. Since MongoDB is regarded as a general-purpose database, it may be applied to a wide range of use cases for various applications. Any major programming language can be used by developers to connect with the database, and MongoDB is compatible with all database deployments, including on-premises, cloud-only, and hybrid. In my opinion, despite the high switching costs, all these flexible features discourage customers from switching database software. This is true even when new data design trends emerge. As is typical practise among databases, MongoDB provides both a free and a premium version of its database. Many rivals, such as Apache Cassandra, Apache HBase, Redis, and RavenDB, also employ an open source business model and provide customers with a free licence. Although the vast majority of MongoDB's 1.5 million free users will continue to be free users, as MongoDB's total number of paying customers is greatly overshadowed at approximately 33,000, the free-to-download version of its database, Community Server, helps drive adoption of its paid subscription version. However, the free edition of MongoDB enables the group of prospective business clients to use it and evaluate its benefits without having to pay anything up front. These customers would be encouraged to choose a premium MongoDB Enterprise Advanced subscription in order to receive more database support. Advanced database security, assistance with setups that optimize availability and scalability, urgent patches, and a commercial license are all included in this support. Any changes made to the source code would need to be made public to the whole MongoDB community if an organization wanted to use MongoDB under the Community Server. Thus, the commercial license protects businesses from having their code made public. Although the commercial license still mandates that the database be ultimately run and maintained by the customer, which is a laborious task, the most compelling reason to purchase a MongoDB Enterprise Advanced subscription is to navigate the enormously complex terrain of databases with ample assistance. However, MongoDB offers MongoDB Atlas, a subscription as a service solution, which can be hosted on any significant cloud and is managed by MongoDB, for users who wish to completely offload the stress of managing their database. Atlas currently accounts for 50% of overall revenue, and according to my analysis, it has similar high switching costs to its Enterprise Advanced product. Data warehouses are not designed to be utilised with MongoDB. Contrary to data lakes, which can store both raw, unstructured data and structured data, data warehouses are repositories of exclusively structured, ready-to-use data. MongoDB doesn't have a data warehouse service, but its data can still be easily loaded into platforms that do. Additionally, MongoDB launched its own data lake solution in 2019. Data lakes are storage areas for both organized and unstructured data that come from various databases and other sources. In order to provide fresh insights, this data in the data lake can subsequently be consumed into artificial intelligence models. These insights are indexed, making them structured data, which is then stored in a data warehouse so that queries can be made with ease. Although MongoDB's data lake is still in its infancy, I believe the same significant switching costs from the company's main database service apply. In reality, given that a data lake is connected to numerous databases, I would anticipate slightly stronger switching for MongoDB's data lake. Therefore, if an organization decided to replace its data lake, it would also need to reconnect to all of its databases, which would likely result in considerably more work than the already difficult overhaul of access method rewriting across software to a single database when swapping out of a database. Snowflake is a well-known, up-and-coming leader in data architectures in the industry. MongoDB is similar to Snowflake in that the database does not necessitate replatformization when a customer wants to switch the cloud service provider on which the MongoDB application is hosted, despite the fact that MongoDB does not compete with the majority of Snowflake's offerings, which center primarily around its data warehouse product. Replatforming can require months of labor and a large financial investment. Therefore, MongoDB clients escape the risk of vendor lock-in from their cloud service provider because to MongoDB's seamless cloud interoperability. Although I don't believe this directly affects switching costs, I do believe it helps to eliminate one more reason why a customer would ultimately want to leave the MongoDB architecture. It's critical to remember that, in my opinion, the database market is not a winner-take-all when considering even the tiny ecology of a single organization. Due to differing needs, businesses use various databases. Management claims that MongoDB often only accounts for a small portion of a company's overall database expenditures. Deleting MongoDB from a single use case inside an organization can be painful, despite the fact that the same enterprise can assert that it utilizes numerous vendors. For this reason, I want to emphasize that I believe strong switching costs exist at the molecular level.


Capital Allocation


Based on my evaluation of a strong balance sheet, extraordinary investments, and mixed shareholder dividends, I believe MongoDB's capital allocation is exemplary. In the future, I believe MongoDB's internal investment plan will produce fantastic shareholder returns. Based on its excellent financial sheet, large cash reserve, and planned debt maturity, I believe MongoDB. At the conclusion of its fiscal year in 2012, MongoDB had $1.83 billion in cash and cash equivalents and $1.14 billion in debt. I don't anticipate any problems with MongoDB repaying its debt over time because it is convertible. Given that the firm is still in its early stages of growth, I believe MongoDB is justified in deferring dividend payments and sizable share repurchases to shareholders. To further advance its first mover advantage before competition engulfs the company, I believe investment back into this business is more crucial than ever at this time.

The firm's investments, in my opinion, are excellent. In order to expand its feature set, MongoDB has undertaken a number of acquisitions, including the 2019 purchases of Realm, an open-source mobile database, and mLab, a cloud database provider. I also think that MongoDB's sales and marketing efforts are a good investment because I think that the great majority of them are focused more on bringing in new, sticky customers than on keeping the ones they already have. Consequently, this has led me to capitalize a significant portion of MongoDB’s sales and marketing expenses.


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