120% EXPANSION RATE

Unstructured data will account for 80% of the 175 ZETTABYTES produced by 2025

NOW the DEFAULT NoSQL database

Share price went from $ 24 to $ 267 in 3 YEARS

Here is an EASY thread

$MDB MongoDB was initially released in 2009 and NoSQL quickly became a good alternative / complement to relational databases

Certainly for the modern web giants that had to deal with increasing amounts of data and real-time web applications

OK… That isn’t very telling Let’s dive into databases

What are relational databases

What are non-relational databases

What are the use cases for relational databases

What are the use cases for non-relational databases

Are non-relational databases taking over relational databases

Is $MDB winning the game

Relational databases organise data into tables that can be linked (also “related”) to each other

When a system or developer needs information It can retrieve it from the different tables where the data is stored into

Storing data on different tables enables for more flexibility and less redundancy

E.g. A shop has information on its customers (Customer ID, name, state, mailing address) and their purchases (Customer ID, purchases, time of visit)

What if it wants to get the names of all the customers that purchased a certain product ?

It can just join the customers AND purchases table on the customer IDs and filter on product

Let’s now say that the shop just wants to send a mail to shoppers in a certain state

It can just solicit the “customers” database instead of getting ALL the data from ALL customers

An alternative would have been to create a huge database with Customer ID, name, state, mailing address, purchases, time of visit

But that would NOT be EFFICIENT

A key aspect of relational databases is that data needs to be saved according to a pre-determined model

These make sure that relational databases are consistent Meaning that readers ALWAYS receive the last updated information or an error

On the downside, a rigid model constraints the structure of the databases

Meaning that you need to determine the columns (variables) for each table and the links (relations) between the tables RIGHT AT THE START

Careful UPFRONT design is thus required and changes have to be carefully implemented thereafter (sometimes including downtime)

This is a problem for data where the relationships change often or when the data lacks structure

Another key weakness is SCALING

When the amounts / complexity of the data you handle changes What can you do?

⇣ VERTICAL SCALING Add more power (CPU, RAM) to make your machines faster

⇢ HORIZONTAL SCALING Add more machines

⇣ VERTICAL SCALING in this case, the data resides in the same node (or database instance - place where the data is saved and can be handled)

Costs rise exponentially when adding CPUs and RAM to ONE machine as complexity rises

E.g. buying one high-performance custom made computer is an order of magnitude more expensive than buying 10 standard and average performance computer

⇢ HORIZONTAL SCALING in this case, the data is distributed over several machines

Costs are kept under control as more “simple” machines can be used as one machine doesn’t need to execute all of the work

What can we do against the

Rigidity of table design in relational databases

Lack of Horizontal scalability

Hint Let’s look at non-relational databases

What are non-relational databases?

Non-relational databases are different as they do NOT store data in tables

Rather, the data can be stored in documents (or graphs, key-value stores or column-based models)

These documents are designed for flexibility as they don’t have a forced schema and data can come in different formats

These documents (equivalent of the “rows” in relational databases”) can be arranged in “collections” (equivalent of the “tables” in relational databases) inside the database

But… Since we can compare documents to rows and collections to tables… isn’t it the same thing?

Well no, the major difference lies in the fact that different documents DO NOT NEED TO SHARE THE SAME STRUCTURE

This is key difference from relational databases where every row has the same fields

In a non-relational database, new information can be added to some documents (records) without requiring a change for all the records

The document’s data can then very easily and efficiently be accessed through a simply key-to-document lookup

WHY are these seemingly weak advantages, KEY in the modern world

First, as the cost of data drastically decreased these last 30 years, the amount of data EXPLODED

AND data came in all shapes and sizes Structured, semistructured, polymorphic

So what do we have here

Data that is unstructured Not best for rigid models

Lots of data Needs to scale

The “document” architecture provides flexibility as documents DO NOT NEED TO SHARE the same schemas

Does it also provide HORIZONTAL SCALABILITY Meaning that we can work with and save larger and larger amounts of data efficiently?

YES

While you cannot in principle split tables (from relational databases) on different nodes You CAN DISTRIBUTE documents (from non-relational databases) over several machines

This enables for horizontal scaling

While the main downside is that some data might be duplicated over several documents

BUT! The costs of these machines is relatively low so this is a lesser problem

But what about these duplicates? From IBM

“That means the information you receive from a query may be incorrect by a few seconds—perhaps up to half a minute. On social media sites, this means seeing an old profile picture when the newest one is only a few moments old...

...The alternative could be a timeout or error. On the other hand, in banking and financial transactions, an error and resubmit may be better than old, incorrect information.”

You can see it here Non-relational databases are good for some websites But others cannot work with “false information”

This is why NoSQL is now said to stand for “Not Only SQL” Where SQL refers to Structured Query Language (language for relational databases)

“Not only” means that non-relational databases OFTEN work alongside relational databases

All in all, there are some use cases where relational databases are a better fit

And some other use cases where non relational databases work best

What are the use cases for relational databases?

Relational databases are best used for tasks that fail as whole OR succeed - This can be said of

ATM machines and online banking applications

Credit card payment processing

Online bookings (tickets, reservation systems)

Health records, inventory management, claims processing…

As you can imagine, a financial transaction needs to succeed or fail It cannot remain in a pending state

What are the use cases for non-relational databases?

Social media

Ecommerce

Gaming applications

Telecommunications

Real time applications

These all have to deal with vast amounts of (un)structured content that often changes such as messages, pictures, comments, live data AND can fail / return an error temporarily

Are non-relational databases taking over relational databases?

According to http://Scalegrid.io  (provides services for non and relational databases)

Around 60% of database administrators rely on SQL and 40% on NoSQL

MySQL is used most (39%), followed by MongoDB (25%) and then PostgreSQL (17%)

44% relied on multiple databases and 75% of these combined SQL and NoSQL to support their products

As we can see $MDB is one of the most popular NoSQL databases

Is the market served by these databases growing

In other words, is the market for UNSTRUCTURED and LARGE AMOUNTS of data growing?

Well, according the IDC the size of worldwide data will reach 175 ZettaBytes by 2025

Representing a CAGR of 61% over the 2019 - 2025 period

80% of that data will be unstructured and 49% of the whole data will be in public clouds

In terms of market size Allied Market Research places the NoSQL market at $ 22B in 2026 - up from $ 2.5B in 2018

Good for a CAGR of 31.4% from 2019 to 2026 Driven by increased demand from

Ecommerce

Web applications

Social gaming

Is $MDB winning the game?

Let’s look at DB-Engines for historical evolution $MDB went from a score of 101 in 2013 to 448 in 2020 for Document Stores (the type of NoSQL database provided by $MDB)

It is leading the pack while other fail to break out e.g. #CouchbaseDB (score of 30) $AMZN (score of 68) and $MSFT (score of 32)

When looking at the bigger picture we can see that established SQL databases are not loosing too much ground

PostgreSQL (relational database) and MongoDB (Non relational database) are both gaining in popularity

It is clear that $MDB is NOT set to replace relational databases These are still widely used

But we have seen that $MDB is leading the document stores databases by a WIDE margin

And searches for “MongoDB Atlas” trend upwards over the last 5 years globally

That is a LOT of information A summary

Non-relational databases are better than relational in some cases (when data amounts are huge, changes over time)

Non-relational databases SCALE way better and are more flexible to real-life data changes

The amounts of unstructured data created each day is BOOMING

$MDB is leading the pack in Document Stores (its own league) by a WIDE margin

So far so good! Let’s have a look at $MDB itself

$MDB generates sales (total of $ 138m) from 3 main sources

Atlas related subscriptions ($ 61m and growing 69% YoY)

Other subscriptions ($ 71m and growing 24% YoY)

Services ($ 5m - stable)

The “Other subscription” is basically the “MongoDB Enterprise Advanced” product offered by $MDB

This enables businesses to run $MDB on their own infrastructure and customise it very finely to their needs

MongoDB Atlas is the one providing the growth behind the $MDB story

It is a cloud Database-As-A-Service that offers a free tier is fully managed provides on-demand scaling and real-time insights

To say that Atlas is an incredible product is an understatement

“MongoDB Atlas, which launched in June of 2016, is a battle-tested database-as-a-service platform (DBaaS) artfully designed and built by the same team that created and continues to nurture and grow MongoDB...

...MongoDB Atlas is a true blessing to the development community; it provides all of the features of its database counterpart, without the operation and heavy lifting normally required when building new applications, letting you focus on what you do best.” - Nick Parsons

The full review is right here https://medium.com/@nparsons08/mongodb-atlas-technical-overview-benefits-9e4cff27a75e

“MongoDB Atlas is a developer's dream database, with a brilliantly simple user interface, more automation than most Database-as-a-Service (DBaaS) solutions, tons of flexibility and controls, built-in replication, and zero lock-in.” - PCMag

You know what is good about $MDB Atlas? It is a usage-based service

Remember the growth in unstructured DATA? Well, $MDB is growing not only by adding more customers

BUT ALSO as its current customers have more data to handle

Here is @cameroniadeluca excellent take on the matter https://twitter.com/cameroniadeluca/status/1204067906503479297?s=20

Financials check

Sales grew by 39% YoY to $ 138m per quarter

Gross margins stand at 69% Down from 72% in prev. Q.

Loss from operations stood at $50m up from a loss of $ 38m a year earlier

Current assets of $ 1.1B vs current liabilities of $ 252m

Long term debt of 935m and spending 10% of revenue on interest expenses

THE BOTTOM LINE

Unstructured data is booming as social media, ecommerce, gaming explode

Non-relational databases SCALE way better and are more flexible to real-life data changes

$MDB has become the default in Document Stores (score of 448 vs 68 for next best) and is consistently gaining in popularity

Atlas is a flawless product, ideally suited to smaller and medium businesses that want the best-in-class AND plug-and-play databases

$MDB is going strong at 44% growth YoY on $ 138m revenue and scoring a net annual recurring revenue expansion rate of 120%

With 68% of sales coming from the USA and UK, there is room to grow globally - certainly with Atlas

We have started a medium stake in $MDB

$DDOG is on our watchlist To Be Reviewed SOON

Please note that this is not a recommendation to buy - You are responsible for conducting your own research

Disclaimer - This is not investment advice in any form and investors are responsible for conducting their own research before investing.

Sources

✑ Investor presentation

✑ Company website

✑ SimilarWeb

✑ DB Engines

✑ IDC

✑ Allied Market Research

✑ IBM

✑ Guru99

✑ Forbes

✑ High Scalability

✑ InfoWorld

✑ Packt

✑ Dataversity

✑ ScaleGrid

✑ Nick Parsons on Medium

✑ PCMag

Hope you liked this thread!

For more content, follow us on Twitter

Want to get UNDER HYPED companies delivered straight to your inbox Don’t MISS IT https://getbenchmark.substack.com