With so many database management solutions currently available, it can be hard to choose the right solution for your enterprise. Here are some common solution comparisons and best use cases that can help you decide.

MongoDB vs. MySQL

MySQL uses a structured query language to access stored data. In this format, schemas are used to create database structures, utilizing tables as a way to standardize data types so that values are searchable and can be queried properly. A mature solution, MySQL is useful for a variety of situations including website databases, applications and commercial product management.

Because of its rigid nature, MySQL is preferable to MongoDB when data integrity and isolation are essential, such as when managing transactional data. But MongoDB’s less-restrictive format and higher performance make it a better choice, particularly when availability and speed are primary concerns.

MongoDB vs. Cassandra

While Cassandra and MongoDB are both considered NoSQL databases, they have different strengths. Cassandra uses a traditional table structure with rows and columns, which enables users to maintain uniformity and durability when formatting data before it’s compiled.

Cassandra can offer an easier transition for enterprises looking for a NoSQL solution because it has a syntax similar to SQL; it also reliably handles deployment and replication without a lot of configuration. However, it can’t match MongoDB’s flexibility for handling structured and unstructured data sets or its performance and reliability for mission-critical cloud applications.

Mobile applications

MongoDB’s JSON document model lets you store back-end application data wherever you need it, including in Apple iOS and Android devices as well as cloud-based storage solutions. This flexibility lets you aggregate data across multiple environments with secondary and geospatial indexing, giving developers the ability to scale their mobile applications seamlessly.

Real-time analytics

As companies scale their operations, gaining access to key metrics and business insights from large pools of data is critical. MongoDB handles the conversion of JSON and JSON-like documents, such as BSON, into Java objects effortlessly, making the reading and writing of data in MongoDB fast and incredibly efficient when analyzing real-time information across multiple development environments. This has proved beneficial for several business sectors, including government, financial services and retail.

Content management systems

Content management systems (CMS) are powerful tools that play an important role in ensuring positive user experiences when accessing e-commerce sites, online publications, document management platforms and other applications and services. By using MongoDB, you can easily add new features and attributes to your online applications and websites using a single database and with high availability. 

Enterprise Data Warehouse

The Apache Hadoop framework is a collection of open source modules, including Hadoop Distributed File System and Hadoop MapReduce, that work with MongoDB to store, process and analyze large amounts of data. Organizations can use MongoDB and Hadoop to perform risk modeling, predictive analytics and real-time data processing.

Over the years, MongoDB has become a trusted solution for many businesses that are looking for a powerful and highly scalable NoSQL database. But MongoDB is much more than just a traditional document-based database and it boasts a few great capabilities that make it stand out from other DBMS.

Load balancing

As enterprises' cloud applications scale and resource demands increase, problems can arise in securing the availability and reliability of services. MongoDB’s load balancing sharing process distributes large data sets across multiple virtual machines at once while still maintaining acceptable read and write throughputs. This horizontal scaling is called sharding and it helps organizations avoid the cost of vertical scaling of hardware while still expanding the capacity of cloud-based deployments.

Ad hoc database queries

One of MongoDB’s biggest advantages over other databases is its ability to handle ad hoc queries that don’t require predefined schemas. MongoDB databases use a query language that’s similar to SQL databases and is extremely approachable for beginner and advanced developers alike. This accessibility makes it easy to push, query, sort, update and export your data with common help methods and simple shell commands.

Multilanguage support

One of the great things about MongoDB is its multilanguage support. Several versions of MongoDB have been released and are in continuous development with driver support for popular programming languages, including Python, PHP, Ruby, Node.js, C++, Scala, JavaScript and many more.

Deployment involves two primary activities: installing MongoDB and creating a database.

Installing MongoDB

• Windows: To install MongoDB in a Windows environment, run Windows Server 2008 R2, Windows Vista or later. Once you’ve decided on the type of database architecture you’ll be using, you can download the latest version of the platform on MongoDB’s download page.
• Mac: When you install MongoDB on macOS, there are two ways you can approach it. As with the install process for Windows-based environments, MongoDB can be installed directly from the developer website once you’ve decided on the type of build you’ll be using. However, the easier and more common method of installing and running MongoDB on a Mac is through the use of the Terminal app, running Homebrew. Click here for more information on Homebrew installations of MongoDB.

Creating a database

After installing MongoDB, you’ll need to create a directory where your data will be stored. This can be done locally or through public or private cloud storage solutions. For more information about getting started with MongoDB, click here for comprehensive guides, tutorials and walk-throughs.