Blog

  Should You Use MongoDB? A Look at the Leading NoSQL Database


Is MongoDB right for your application? That depends on the kind of data you have, and what you want to do with that data.
NoSQL databases have risen to the occasion when it comes to unstructured modern data and the kind of real-time analytics we expect from it. Depending on your own unique business needs, MongoDB might be just the right solution for you. In this article, we’ll cover the basics of NoSQL databases and some of the challenges of modern big data analysis that MongoDB is particularly well-suited to tackle.

How MongoDB Is Changing the Business of Big Data

Modern data is vast, unstructured, and sometimes unwieldy. It’s big and complicated, and we have big expectations for what it can do—and trying to store, process, and analyze all of it has led to the development of NoSQL databases. These document-based databases eschew the table-based structure of relational databases and let you group data more logically—read more about this in our Guide to Database Technology article.
The leading NoSQL database, MongoDB, has come out ahead in the field for a few reasons. It’s the database component used in the MEAN software stack, it’s open-source, and it’s cross-platform compatible. It also has some impressive built-in features that make it an excellent choice for businesses that need fast, flexible access to their data, whether it’s to make real-time, on-the-fly decisions, or to create tailored, data-driven experiences for users. It’s not just limited to the MEAN stack, either—it’s compatible with .NET applications, the Java platform, and more. It’s been used by organizations like Metlife, ADP, The Weather Channel, Bosch, and Expedia.
When all of the other components of your networked application are designed to be fast and seamless, your data shouldn’t be the bottleneck—and MongoDB is able to meet new data challenges that are difficult (if not impossible) to accomplish well with relational databases. MongoDB enables you to:

  1. Store large volumes of data that often have little to no structure. Relational databases store structured data like a phonebook. But for growing, unstructured data—for example, a customer’s preferences, location, past purchases, and Facebook likes—a NoSQL database sets no limits, and allows you to add different types of data as your needs change. Because MongoDB is flexible and document-based, you can store these JSON-like binary data points (referred to as BSON) in one place without having to define what “types” of data those are in advance.
  2. Make the most of cloud computing and storage. Cloud-based storage is an excellent cost-saving solution, but requires data to be easily spread across multiple servers to scale up. MongoDB can load a high volume of data and give you lots of flexibility and availability in a cloud-based environment, with built-in sharding solutions that make it easy to partition and spread out data across multiple servers.
  3. Develop and release quickly. If you’re developing within two-week Agile sprints, cranking out quick iterations, or needing to make frequent updates to the data structure without a lot of downtime between versions, modifying a relational database will slow you down. With MongoDB’s dynamic schemas, you can try new things, and fast. Your data doesn’t need to be prepped ahead of time, and your team can incorporate anything new, quickly, and at a lower cost.
  4. Scale database architecture efficiently and inexpensively. With MongoDB, it’s easy to spread data out across commodity hardware on-site or in the cloud without needing additional software.

MongoDB Helps You Make the Most of Modern Data

The data environment has changed a lot since SQL was first designed in the 1970s, and a few of MongoDB’s most popular uses are great examples of how its NoSQL capabilities can meet the challenges of modern data.

  • Location-based data analytics and operations.If you gather location-based data, MongoDB has built-in spacial functions that allow you to harvest this data from specific locations and put it to use without complicated extraction processes.
  • Get real-time data reporting and analytics. Pull together data from across different silos seamlessly, and get a real-time, snapshot view of your data that you can use. Trying to consolidate different types of data like this with a relational database is a challenge.
  • Capitalize on sensor data and connected devices—or, ‘the Internet of Things.’ Billions of sensors and connected assets and devices are creating millions of data points, and it’s a challenge for relational databases to absorb and analyze without time-consuming ETL (extract, transform, and load) processes. MongoDB can analyze data of any kind within the database itself.
  • Powering content management systems (CMS). Whether your CMS is designed for e-commerce or publishing content, MongoDB is an ideal partner because it can house so many different types of data—the data that drives the necessity of a CMS-powered site. Store and incorporate multimedia, tweets, comments, and more.
  • Push out new versions of mobile apps fast. MongoDB’s ability to support fast iterations means you can scale up, make modifications, and give your customers new and better apps fast, and without the cost of updating your relational database management system (RDBMS). Being able to scale horizontally and handle unstructured data also makes it an ideal pairing for mobile app development.
  • Personalize data to tailor user’s experiences. Give customers a personalized experience based on what you already know, and what you can predict. MongoDB can give you a real-time analysis of a customer that you can translate into a tailored experience by harvesting data like browsing history, demographics, behaviors, interactions, and more.

Is MongoDB Right for Your Application?

Every data environment is different. They’re built to meet unique business challenges. Depending on your organization’s needs, MongoDB may be the best solution over an RDBMS, or it may be better suited to tackle specific jobs while your RDBMS handles your more structured data.
There are a few other things to take into consideration when weighing your database options:

  • Your team’s existing skill sets and tools. How invested are you in your current RDBMS? How SQL-heavy is your team’s skill set? If you’re a smaller company with more budget constraints, bear in mind that MongoDB is one of the easier NoSQL databases to set up and manage.
  • Your existing architecture and network infrastructure. MongoDB can be layered into your current data environment for a hybrid approach.
  • Location-based requirements. Need to know where your data is coming from, or pull data from a specific location? MongoDB’s powerful query system lets you leverage this well.
  • Security requirements. It’s important to set up your MongoDB database with the security guidelines, especially if it’s on an Internet-accessible server like a cloud hosting service. If you have very sensitive customer data, take extra security measures into account.
  • How much you expect your data to grow (and how soon). This could be a major driver in your decision to go with MongoDB because it’s able to scale horizontally so well. If you’re looking to house truly massive sets of data and need compression tools, however, you might opt for the Cassandra NoSQL database.


Leave a Reply