Real-Time Insights
Full Data Lifecycle
Data-Driven Teams
Why use MongoDB for Data Analytics?
Accelerate Insights
Flexible Data Model
Store and combine data of any structure and modify schemas dynamically as your data evolves using MongoDB’s flexible data model.
Powerful Aggregation Framework
Build consumable data processing pipelines with MongoDB’s expressive query language and the powerful aggregation framework.
MongoDB Charts
Natively visualize MongoDB data, embed charts into your applications, and build live dashboards for collaboration with MongoDB Charts.
Intelligently Tier Your Data
Atlas Data Lake
Natively query data of any format across Amazon S3 and MongoDB Atlas in-place using MongoDB’s fully managed, serverless Atlas Data Lake.
Distributed Processing
Process your data in parallel with a query framework that is aware of data locality and optimizes in-place computation to reduce data movement.
Workload Isolation
Serve multiple workloads simultaneously without data duplication or performance impact using workload isolation or Atlas analytics nodes.
Leverage a Rich Ecosystem of Tools and Machine Learning
MongoDB Connector for BI
Create rich visualizations using SQL-based business intelligence tools with MongoDB Connector for BI, eliminating the need for complex ETL processes.
MongoDB Connector for Spark
Enable machine learning development with MongoDB Connector for Spark which exposes all of Spark’s libraries and APIs.
MongoDB Realm
Innovate faster by plugging into the leading cloud machine learning and AI services with MongoDB Realm service integrations.
MongoDB Drivers
Unleash the power of Python, R, or an array of other idiomatic drivers and frameworks with MongoDB Drivers.
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