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Real-Time Analytics

Real-time analytics for better customer engagement

Customer behaviors are rapidly evolving, supply chains are reorganizing, and employees are working in new ways. Businesses need to provide more personalized customer experiences, react more quickly to market trends, and detect and prevent potential problems. But few can respond to changes in data minute by minute or second by second.

With MongoDB, businesses can analyze any data in place and deliver insights in real time. That gives organizations new capabilities, including:


  • Capturing streaming or batch data of all types without excessive data mapping
  • Analyzing data easily and intuitively with a built-in aggregation framework
  • Delivering data insights rapidly and at scale with ease


By combining data from real-time events with historic and reference datasets, organizations can optimize queries to quickly deliver actionable results. This translates to better insights — and better customer engagement.


Applications built with real-time analytics

A diagram of showcasing real time analytics applications


From personalized offers on a retail website to your banking app alerting you that there has been fraudulent activity in your account, real-time analytics power applications in ways big and small. Often surfaced as a microservice within another application, real-time analytics are most commonly presented in four ways:

Personalization: Real-time analytics can be used to evaluate user behavior, present profile information, and call up historical interactions to better tailor and enhance customers’ experiences or help with a decision in real time.

Fraud and error prevention: Real-time analytics can help identify fraudulent activity and clerical errors by matching existing information with the current situation. Because of the immediate nature of real-time information, instantaneous action can be taken to prevent deceptive practices.

Performance optimization: Real-time analytics can help you make just-in-time adjustments to processes and activities to optimize for better performance and resource allocation.

Preemptive maintenance: Real-time analytics can aid in optimizing systems and machines, improving performance and productivity along the way to reduce the chance of costly downtime and loss of productivity.


Building real-time applications with MongoDB’s developer data platform

A diagram showcasing the various data sources used to create real time applications.

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

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  • CRUD
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  • Full-text search
  • Language drivers