![Sai shivan J.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Sai shivan J.") SJ Sai shivan J. Associate Consultant Mid-Market (51-1000 emp.) 3/24/2026 "Flexible, High-Performance Database with Easy Scaling" 4.5/5 What do you like best about MongoDB Atlas? Best about MongoDB is its flexible document schema that lets me store JSON-like data without rigid table structures, perfect for my data analyst work. MongoDB delivers sub-100ms real-time data retrieval speeds, making it incredibly fast for querying and analyzing large datasets. The horizontal scaling through sharding lets me easily handle growing data volumes without performance drops, which is essential for event data management systems. MongoDB's native JSON/BSON document model means I can work with data in the same format I use in my code, eliminating conversion headaches and boosting developer productivity. Rich ad-hoc queries, powerful indexing, and built-in aggregation pipelines let me perform complex real-time analytics and data transformations directly in the database Review collected by and hosted on G2.com. What do you dislike about MongoDB Atlas? One thing I dislike about MongoDB is that it doesn't support multi-document ACID transactions as robustly as traditional SQL databases, which can be problematic for applications requiring strong consistency across multiple operations. MongoDB's memory usage can be quite high since it relies heavily on RAM for caching and performance, requiring more infrastructure resources compared to some other databases. The lack of native joins means I often have to handle data relationships in application code rather than at the database level, which adds complexity to queries and can impact performance. Additionally, data duplication is common in MongoDB due to its denormalized document model, leading to increased storage requirements and potential data consistency challenges when updating duplicated fields across multiple documents. Review collected by and hosted on G2.com. What problems is MongoDB Atlas solving and how is that benefiting you? MongoDB solves the problem of rigid, fixed schemas in traditional databases by providing a flexible document model that lets me store evolving data structures without complex migrations, speeding up my development significantly. It solves scalability challenges through horizontal sharding, allowing me to handle massive volumes of meter data and event records without performance degradation, which is crucial for my MDMS work. MongoDB's high availability through replica sets ensures zero downtime for my applications, automatically failing over to secondary nodes if the primary fails. The database solves the impedance mismatch problem between code and data by storing JSON/BSON documents that match my application's data structures, eliminating conversion overhead and boosting my productivity as a developer. MongoDB's powerful aggregation framework and indexing solve complex data transformation and analytics needs directly within the database, enabling me to perform real-time data analysis without external processing tools Review collected by and hosted on G2.com. Show More Rating Updated (5/27/2026) Current UserValidated ReviewerSource: Organic See what 849 reviewers think of MongoDB Atlas 4.5 out of 5 ยท Verified reviews from real users [ Read all reviews ](https://www.g2.com/products/mongodb-atlas/reviews)