To use MongoDB or not: Choosing the correct database is an important step when developing a product. That’s why we’ve put together this helpful breakdown to help you determine whether MongoDB is the right tool for the job.
Being a NoSQL (Non-Structured Query Language) database, one of MongoDB’s defining features is its schema-less or non-relational data structure. This allows for a huge degree of versatility in storing various data types and accessing them on the fly.
Versatility is especially important nowadays with the commoditization of Big Data, which is generated from countless different sources and doesn’t always fall into neat categories. We’ll explain what Big Data is and why it matters in the next section.
Another advantage MongoDB offers is the opportunity for horizontal scaling through sharding.
Since stored data isn’t structured vertically, it can be spread, or “sharded,” over multiple commodity servers, with the option to easily add more as necessary.
Moreover, sharding makes the hardware side of things easier as it lightens the required storage and processing power for a single machine.
Related to horizontal scaling is MongoDB’s speed. Its workflow for submitting query keys is simpler than in SQL since it doesn’t require specifying a schema – simply index the datapoint you’re looking for and MongoDB will retrieve it.
The lack of a set relational structure means that submitting a query requires far less processing power to search and retrieve than with a relational database.
MongoDB’s non-relational structure allows comparatively small companies to store, access, search and analyze massive amounts of data, increasing the scope and breadth of their business solutions and making it easier to scale.
MongoDB supports all major programming languages (Ruby, PHP, Java, etc.), and has numerous community-supported drivers for lesser-known programming languages as well.
It can be hosted by its own cloud service, MongoDB Atlas, and offers both a community-driven open source and a premium Enterprise Edition. (Our GUI and IDE for MongoDB, Studio 3T, works with any of these deployments.
These different product tiers allow developers to easily familiarize themselves with the software, a lifesaver for startups with limited capital to figure out if MongoDB is suitable for their business plan.
Studio 3T expands MongoDB’s accessibility even further with SQL Migration, allowing users to import and export SQL tables and their relationships to and from MongoDB.
As you may have guessed, MongoDB’s non-relational horizontal scaling allows for a huge degree of operational flexibility.
This makes it a useful platform for experimenting with new, unconventional content models. It also makes it invaluable for those with changing content requirements, such as advertisers.
One of the major reasons MongoDB has been so widely adopted is the ease at which it can integrate into a development pipeline – developers don’t need to learn SQL or hire a Database Administrator to make full use of its functionality. Add to that tools like Studio 3T, which can help the whole team query MongoDB without prior knowledge of the MongoDB query language.
As of v4.0 in mid 2018, MongoDB supports multi-document ACID (atomic / consistent / isolated / durable) transactions.
Transactions guarantee that data transfers happen either successfully or not at all. In the past, banks and other large organizations were cautious to use MongoDB because of its lack of transactional integrity.
Transactions are the key reason why MongoDB has rapidly crossed the chasm from niche software to market-disrupting mainstream database platform.
Now that we’ve outlined MongoDB’s advantages, let’s take a look at some potential use cases.
One of MongoDB’s most prominent possible use cases is, as mentioned above, Big Data. The term itself refers to mass volumes of data that are too large, fast-moving and computationally complex to be processed by traditional, hierarchy-based data processing software.
Big Data has become an increasing phenomenon over the past decade or so as cloud computing, apps and online services have become more ubiquitous, alongside increasing processing power and storage. All of this accumulated data has massive analytics potential in a wide range of fields including finance, meteorology, aviation, online retail, genetic research, demographic studies and more, which is where MongoDB comes in.
MongoDB’s NoSQL and non-relational structure is perfectly suited to the four Vs of Big Data: Volume, Variety, Velocity and Veracity:
- Volume refers to the quantity of the generated and stored data. Generally, volume is the most important metric that measures a dataset’s quality and whether that dataset can be defined as Big Data to begin with.
- Variety refers to the type composition of the dataset, which can include text, audio, video, images, search queries, daily travel routes and more. Data can be grouped across mediums by topic to provide various insights, and to complete information gaps that would appear with only one or two data-types. This is known as “data fusion.”
- Velocity refers to the speed at which data is generated and analyzed, which in Big Data’s case is often continuous and in real-time. For example, so much as a 10 second lag in tracking the path of a tsunami across interlocking waterways and infrastructure can be lethal if the wave is travelling at 80kmph. Huge volumes of data need to be converted into on-the-ground evacuation orders in real-time, not 60 seconds ago.
- Veracity refers to the quality of the data, such as where it was generated, its level of detail or ambiguity, how well it can be replicated and so on. Individual data quality can vary wildly with Big Data due to the sheer number of sources, which is why volume, as mentioned above, is often considered the main indicator of quality to determine the most accurate results.
MongoDB isn’t just suited for processing massive volumes of data – its strengths can apply to an application of any size that requires processing varied data types from various sources.
As touched on above, one of MongoDB’s greatest strengths is Big Data analytics. Analytics can be on any scale, however. The internet has allowed a lot of niche interest groups to organize and flourish, with businesses catering to these new small but globe-spanning customer bases.
An example could be, say, historical reenactors on the market for period-accurate clothing and props; MongoDB can compile and analyze the exact preferences of these consumers and tailor a business model accordingly. One advantage to engaging with niche interests is that the quality of each individual datapoint gathered is often more precise, and therefore of higher quality, than with broader topics.
Another use case for MongoDB is for powering an online store or e-commerce solution. MongoDB’s features can easily manage attributes in a product catalogue, track the interactions between the store’s inventory and customers’ shopping carts, and offer dynamically switching recommendations such as “Customers also bought” in a single shopping session.
For larger store inventories MongoDB can also model and store convenient product hierarchies within different categories.
MongoDB offers a robust platform to store content when building content management systems (CMS) for websites, particularly those with a wide variety of text, images, videos and plugins to organize. Some examples are Nike and L.L. Bean, who are powered by MongoDB and Studio 3T.
MongoDB can also store user-generated content such as comments, which can then be easily moderated and analyzed to draft guidelines for future content.
MongoDB is an excellent option for mobile development thanks to its horizontally-scalable database structure, even more so since acquiring Realm in April 2019.
Mobile software has to be dynamic and scalable by design due to the constantly shifting mobile market – an app must be able to accommodate a potential rapid influx of users and bandwidth as it gains popularity.
Any use case that requires large volumes of high-speed data logging and aggregation is a perfect fit for MongoDB.
Large organizations such as airlines and GPS providers in particular are always in pursuit of higher efficiency, not to mention more effective monitoring and early warning methods for their complex systems.
Lastly, any organization with open-ended objectives or that deals with unknown outcomes (such as a branch of the military during an operation or a news agency compiling voter statistics during an election) can find plenty of use for MongoDB.
MongoDB is also useful for any scenario where large sections or even an entire design framework may change over time, such as a mechanical engineering pipeline or in mobile development, as mentioned above. A specific case study would be SEGA, whose teams use Studio 3T to manage video game development’s notoriously demanding parameters.
Companies and organizations that already make use of Studio 3T-powered MongoDB range across the spectrum, from large airlines and software companies like Air France and Intel to supermarkets and search engines like Whole Foods and Yahoo.
The applications of MongoDB are truly endless in the digital era. Should you decide that MongoDB is the right database for the job, we hope you choose the right GUI.