MongoDB Advantages: 12 Reasons Enterprises Choose It

MongoDB Benefits: Why Businesses Choose a Document Database

Relational databases make the assumption that you have a fixed structure for your data, and that it will change very little over time. That’s a concept broken day by day in modern applications, with new fields added to user profiles, new product attributes attached to a product, and data from IoT devices flowing through without fitting into any hard-wired table. That’s what was meant to be done by MongoDB and that’s why it is the world’s most popular document database.

The 12 MongoDB benefits that are most significant in a real enterprise environment — and when MongoDB isn’t.

What Is MongoDB?

MongoDB is a NoSQL document database that uses flexible, JSON-like documents (BSON) to store data, rather than rows and columns. Information is grouped together in a single document, instead of being spread across joined tables, and that maps to the way that applications use data.

12 MongoDB Pros & Cons

1. Flexible, Dynamic Schema

Not all fields will be the same for documents within a collection. Looking for a “loyalty tier” mechanism for customers? Just get it written, no schema migration, downtime, no ALTER TABLE on millions of rows.

2. A Data Model Developers Actually Think In

A customer and their addresses, preferences and orders can all reside in one document, rather than in a five table join. Development becomes quicker and coding becomes easier.

3. Horizontal Scalability by Sharding

If a single server isn’t sufficient, MongoDB allows you to spread data across multiple servers via sharding. The economics that made web-scale companies possible is that capacity is added by adding commodity servers instead of ever-larger machines.

4. Built-In High Availability

Replica sets maintain multiple copies of your data that are all synchronized. In case of failure of the primary node, an election automatically elects a secondary node, usually within seconds, and without any changes to the application.

5. Excellent Performance on Read/Write-Intensive Workloads

Access patterns that are common are stored together, and are not subject to costly joins. In combination with rich indexing (compound, geospatial, text, wildcard), MongoDB is able to deal well with high throughput workloads.

6. Powerful Aggregation Framework

The aggregation pipeline processes the data in the database itself: filtering, grouping, transformation and analytics, all within the database without requiring the shipment of data to the external processing layers.

7. Multi-Document ACID Transactions

Multi-document ACID transactions, as part of MongoDB 4.0, are a long-standing criticism that has been addressed.A long-standing criticism that has been addressed: multi-document ACID transactions, now supported by MongoDB 4.0, allow operations that span multiple documents to commit or roll back as a single unit when correctness requires it.

8. Native Handling of Diverse Data

JSON documents, arrays, nested objects and geospatial coordinates, time-series data – MongoDB stores and queries them natively, making them a good choice for catalogs, sensor data, content platforms and event streams.

9. MongoDB Atlas and Multi-Cloud Freedom

The managed cloud service, Atlas, is available on AWS, Azure and Google Cloud, supported by an automated backup, scale and monitor service. Enterprises benefit from the flexibility of the cloud, but without the operational burden, or single-cloud lock-in.

10. Oracle Data Server-side Mirroring and Oracle Replication Server-side Mirroring

Applications can subscribe to changes in data in real time without polling, which drives live dashboards, notifications and event-driven architectures.

11. Massive Community and Ecosystem

Official drivers for all major languages, copious documentation and one of the largest developer communities in the database world means answers, talent and tooling are easily accessible.

12. Reduce the Cost of Change

The one thing perhaps not mentioned enough: When business needs change, MongoDB changes along with it; no schema-migration project to slow down the Relational. The speed at which things happen, is a competitive advantage.

In some situations, you should use MongoDB. In certain cases, it is best to use MongoDB.

• Product catalogs containing different attributes.
• Personalization information, user profiles
• Content management and mobile-app backends.
• Ingesting time series data at scale with IoT
• Real-time analytics and event-driven systems.
• Fast-changing applications with frequent changing requirements

When MongoDB Is Not the Best Fit

A dependable evaluation is inclusive of the other party. MongoDB may not be the best choice when:

• You have many-to-many relationships that are deeply relational with many queries being performed in either direction (a relational database may be more appropriate for you).
• You have complex multi-row transactions that you frequently rely on (e.g., core banking ledgers, etc.) — stick with the traditional RDBMS platforms.
• Your team’s reporting stack is completely SQL-based and cannot change its reporting patterns.

Many enterprises have MongoDB as one data source with Oracle, SQL Server or PostgreSQL as another, each one working in its own sweet style. A key element of good architecture is understanding the trade-offs inherent in the advantages of the various DBMSs (see our comparison of the advantages of DBMSs).

Frequently Asked Questions

Why is MongoDB better than relational databases?

Flexibility in the schema and horizontal scalability. Without migrations, MongoDB can evolve its data structure based on the ever-changing requirements, and scales out across servers, and its document model ensures that related data is not split and therefore can be accessed quickly and without joins.

Is MongoDB right for big enterprises?

Yes. MongoDB supports mission-critical workloads for some of the world’s largest enterprises with replica sets, sharding, ACID transactions, enterprise security features and the managed Atlas service.

Which is faster, MongoDB or SQL databases?

When access patterns are document-oriented (reading/writing an independent record), often yes, since joins are avoided. If analytics need to be highly relational, then a tuned SQL database can take the win. Remember, performance is always dependent on data model and workload.

Is it possible to move from a relational database to MongoDB?

Yes, but it’s a remodeling exercise, not a copy-paste: tables should be thought of as documents. A proper and structured data migration strategy should be in place with relevant testing.

Get MongoDB Right with RalanTech

Adopting MongoDB delivers these advantages only when the data model, sharding strategy, indexes, and security are designed correctly from the start. RalanTech’s database experts provide database consulting, modernization, migration, and 24/7 managed support — helping you choose, implement, and operate the right database for every workload.

Schedule a free consultation or call +1 813-600-3297 to discuss your MongoDB or database modernization project.