Fresh data for business decisions and in-product analytics for end users
Best of SQL and NoSQL
Structured and semi-structured data in one database
Generative AI on JSON Data
Run vector and semantic searches with dot_product and euclidean_distance on JSON data.
Overview
MongoDB
Description
Built for fast ingest, high concurrency, low latency queries to help you transact, analyze and contextualize data in real time. Available in the cloud, on Kubernetes or on-premises.
Document-oriented database that stores your records as key-value pairs.
Database Model
Relational DBMS
Non-relational or NoSQL
DBaaS Offering
SingleStore Helios
MongoDB Atlas
Flexible Data Model
Data Model
Multi-model: SQL for CRUD; Support for vector search, full text, time-series and geospatial data. SingleStore Kai™ for MongoDB® achieves 100x-1,000x faster analytics for MongoDB apps.
Document-oriented NoSQL model lacks the relational set of capabilities required for analytics
3 / 5
Performance
Analytical Performance
100x faster analytics with no query or application changes
Data typically needs to be moved to another database for analytics, which can be costly and time-consuming. It also requires writing queries in a different language.
Scalability
Architecture
Scale-out architecture efficiently and quickly responds to growing workloads leveraging commodity hardware — without add-ons or specialized tuning expertise.
Offers secondary indexes; however,the indexes don't scale with sharding and the system is suboptimal at loading large amounts of random data, as the underlying file format is not truly efficient.
5 / 5
3 / 5
Cost-Effectiveness
Total Cost of Ownership (TCO)
Transparent pricing model with a simple, single-product subscription for all of your workloads which is easy to manage and patch, and delivers maximum performance on a familiar SQL data model at a reasonable cost
Additional costs for OLAP database and ETL tooling required to prepare and move data for analytics and machine learning use cases.
