MongoDB (link resides outside IBM) is an open source, nonrelational database management system (DBMS) that uses flexible documents instead of tables and rows to process and store various forms of data. As a NoSQL database solution, MongoDB does not require a relational database management system (RDBMS), so it provides an elastic data storage model that enables users to store and query multivariate data types with ease. This not only simplifies database management for developers but also creates a highly scalable environment for cross-platform applications and services.
MongoDB documents or collections of documents are the basic units of data. Formatted as Binary JSON (Java Script Object Notation), these documents can store various types of data and be distributed across multiple systems. Since MongoDB employs a dynamic schema design, users have unparalleled flexibility when creating data records, querying document collections through MongoDB aggregation and analyzing large amounts of information.
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.
MySQL (link resides outside IBM) 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.
While Cassandra (link resides outside IBM) 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.
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.
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 (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.
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.
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.
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.
Deployment involves two primary activities: installing MongoDB and 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 (link resides outside IBM) for comprehensive guides, tutorials and walk-throughs.
Learn more about IBM Cloud Databases for MongoDB, a powerful document data store designed for developer ease of use and flexibility.
Make running MongoDB simple: Automate administration, stay on top of database performance and unlock opportunities to turn data into value.
Collect, organize and analyze data, no matter where it resides. Predict outcomes faster using a platform built with data fabric architecture.
NoSQL is an approach to database design that enables the storage and querying of data outside the traditional structures found in relational databases.
Get an introduction to the important aspects of cloud storage, including how it works, its benefits, and the different types of cloud storage that are available.
Load balancing distributes network traffic evenly across multiple servers to prevent failure caused by overloading a particular computing resource.
For organizations seeking a better solution for managing their NoSQL databases while integrating into a multicloud environment, IBM Cloud® Databases for MongoDB provides a flexible and scalable solution for all their enterprise needs. By leveraging MongoDB’s powerful indexing and querying capabilities with IBM’s fully managed, secure cloud configurations, they get a highly sustainable and secure solution for enterprise database management.