Capture data from multiple sources
Real-time data reflects what is happening now. It includes event-driven and streaming data — for example, user activity on a retail site or within a banking app, or sensor data within an IoT application. Historical data reflects events or inputs that happened in the past — for example, customer profiles, purchase history, or shipments. There’s a good chance that you offload historical data into a data warehouse or cloud storage, such as an Amazon S3 bucket.
- With MongoDB, you can capture data from multiple sources into a single view. MongoDB:
- Supports multiple data structures and types with the industry-leading multimodal data platform
- Easily adjusts to new data types with a flexible schema and JSON-like document model that allow for different fields from document to document
- Seamlessly ingests cloud storage data with traditional batch processes and event-driven data with the MongoDB Connector for Apache Kafka (with support for time series data)
Combine, enrich, and analyze data
With MongoDB, real-time analytics can be derived from multiple data sources — from basic aggregations to machine learning and AI — and stored separately. The analysis can be done on fresh data at scale and with high integrity.
MongoDB’s capabilities include:
- Performing analysis and data preparation via the MongoDB aggregation framework, including window functions on time series data
- Tightly integrated partner solutions for AI/ML, plus the MongoDB Connector for Apache Spark for more advanced analytics
- Cost-effective and efficient horizontal scaling with sharding, plus the ability to maintain high operational performance with workload isolation
- ACID-compliant databases to ensure the ability to react to new data in real time and maintain high data integrity while serving many concurrent queries
Deliver action-driven insights
Whether you’re preventing fraud or sending personalized offers, timeliness is crucial to the success of your app and, ultimately, your business. Insights must be delivered as they happen.
Configuring and developing real-time analytics with high productivity — meaning less time wasted mapping data tables or coding single-use data pipelines — means you’re making your data a competitive advantage.
- Offers a variety of efficient options for delivering insights to data consumers in real time, including change streams, triggers, and GraphQL.
- Makes it easy for developers to code insights into apps with their preferred language via the MongoDB Query API
- Integrates full-text search, data visualization, and data lake use cases in a simple architecture
- Provides transactional processing and powerful indexes to ensure low-latency queries