MongoDB Enterprise Advanced is a comprehensive platform renowned for its scalability, user-friendliness, and high performance, underpinned by its flexible document-based storage and open-source model. JSON compatibility, clustering, and security elevate its standing among professionals.
| Product | Market Share (%) |
|---|---|
| MongoDB Enterprise Advanced | 15.1% |
| ScyllaDB | 10.0% |
| Cassandra | 9.7% |
| Other | 65.2% |
| Type | Title | Date | |
|---|---|---|---|
| Category | NoSQL Databases | Aug 27, 2025 | Download |
| Product | Reviews, tips, and advice from real users | Aug 27, 2025 | Download |
| Comparison | MongoDB Enterprise Advanced vs ScyllaDB | Aug 27, 2025 | Download |
| Comparison | MongoDB Enterprise Advanced vs Microsoft Azure Cosmos DB | Aug 27, 2025 | Download |
| Comparison | MongoDB Enterprise Advanced vs InfluxDB | Aug 27, 2025 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| PostgreSQL | 4.2 | N/A | 96% | 125 interviewsAdd to research |
| Redis | 4.4 | 9.2% | 100% | 23 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 34 |
| Midsize Enterprise | 12 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 160 |
| Midsize Enterprise | 77 |
| Large Enterprise | 351 |
The platform facilitates efficient data management through developer-friendly tools and a strong aggregation framework. MongoDB’s no-schema requirement, supported by community expertise, underlines its adaptability. While its sharding capabilities and affordably support large data volumes, there are aspects such as security enhancement and enterprise tool integration that need attention. Indexing and query optimization pose challenges, alongside high costs. Improvements in analytics and UI could advance its infrastructure further.
What are the key features of MongoDB Enterprise Advanced?Industries leverage MongoDB Enterprise Advanced for significant roles in data storage within IoT platforms, healthcare apps, public service monitoring, and big data analytics. Companies in logistics and telecommunications find it instrumental for business process management and video content management, benefiting from its seamless integration and unstructured data support.
Facebook, MetLife, City of Chicago, Expedia, eBay, Google
| Author info | Rating | Review Summary |
|---|---|---|
| Architecte Cloud at Visiativ SA | 3.5 | I find MongoDB suitable for legacy applications due to its stability and performance, especially in ReplicaSet models. However, it's costly and lacks vector database support. Cosmos DB is preferred for modern AI projects and easier sharding. |
| Head Of Analytics at Mjunction Services | 4.0 | We use MongoDB in a business unit with an ETL pipeline for data extraction. Its flexibility and scalability suit our needs. However, improvement is needed for AI solution integration. We switched from IBM DB2 for a composite database system. |
| General Manager at Lytwave | 4.0 | I've found MongoDB beneficial for improving our network monitoring and reporting dashboards. While not suitable as a standalone solution, it's excellent within templates like LibreNMS. The documentation could be more extensive, and I'm curious about alternative solutions such as Cisco or Linux. |
| Full stack developer at a non-tech company with self employed | 4.5 | No summary available |
| OCI/AWS Consultant at a government with 11-50 employees | 5.0 | I use MongoDB for document-based databases, particularly in remote areas for unemployment forms via Form IO. Its flexible schema, replication, and scalability are valuable. Previously using SQL, I found MongoDB excels in handling unstructured data. |
| Director at Marsh | 4.0 | I use MongoDB for storing application data in JSON format, appreciating its lightweight performance and replication features. However, improvements are needed in transaction capabilities and sharding complexity. We're transitioning to Atlas Cloud, and previously considered PostgreSQL. |
| Senior Consultant at Infosys | 4.5 | I used MongoDB in a production-level project with 30 team members, valuing its flexibility and fast performance over Oracle DB and MS SQL. However, integration with custom code could improve, and bundling with Kibana would simplify installations. |
| Senior Software Engineer at a computer software company with 10,001+ employees | 4.0 | We use MongoDB primarily for handling large volumes of data due to its scalability and user-friendly interface, especially with Atlas. While it's flexible and intuitive for experienced users, improvements in query optimization and beginner-friendly documentation are needed. |
