Exploring Embedded Analytics

Embedded analytics integrates data analysis and visualization capabilities directly into end-user applications, enabling users to access insights within their workflows without switching to standalone BI tools. Unlike traditional analytics, where users log into dedicated platforms for dashboards or reports, embedded analytics delivers actionable insights seamlessly within the context of the user experience—all without leaving the application.

Embedded analytics use cases

Embedded analytics seamlessly integrates into workflows, serving both external-facing users and internal operations. For external-facing workflows, embedded analytics enhances customer experiences by delivering actionable, real-time insights within applications. For instance, in a ride-sharing app, embedded analytics powers features like dynamic pricing based on demand, estimated pickup times calculated from live traffic data, and driver availability updates. These insights provide users with transparency and improve their overall experience by setting clear expectations and enabling informed decisions. 

Internally, embedded analytics can drive operational efficiency by enabling teams to respond swiftly to changing conditions. For example, in a supply chain management system, embedded anomaly detection can identify bottlenecks, such as delayed shipments or production slowdowns, and alert managers in real time. This allows immediate corrective actions, like rerouting logistics or adjusting schedules, ensuring smoother operations and reduced downtime. By embedding analytics into both external and internal workflows, organizations empower users and teams to make faster, data-driven decisions.

How embedded analytics differs

While traditional analytics focuses on broad reporting and exploration in standalone tools, embedded analytics is inherently operational and user-specific. It requires the analytics engine to interact with the application’s front-end in real time, delivering insights that drive immediate decisions. This contrasts with offline or batch analytics, where data latency isn’t critical, and the focus is more on historical trend analysis.

The need for low query latency

Embedded analytics must meet strict performance requirements. Users interacting with embedded analytics expect instant responses to queries, often within milliseconds, to maintain a fluid application experience. For example, consider a food delivery app showing nearby delivery trends; even a second of lag can degrade the user experience. This demand for ultra-low query latency is a defining challenge of embedded analytics, requiring robust, real-time data architectures.

Apache Pinot and StarTree: Purpose-built for embedded analytics

Apache Pinot, a distributed OLAP database, is uniquely designed to meet the high-speed, low-latency demands of embedded analytics. Its ability to handle real-time ingestion and deliver millisecond query responses at scale makes it ideal for modern applications. StarTree, built on Pinot, extends these capabilities with a managed, enterprise-ready solution that simplifies adoption and scalability, making it the perfect fit for delivering exceptional embedded analytics experiences.

Apache Pinot powers embedded analytics for some of the world’s leading companies. LinkedIn uses Pinot to deliver real-time insights in features like “Who Viewed Your Profile” and job analytics. Uber leverages Pinot to provide driver performance metrics and operational insights within its app. Stripe integrates Pinot for financial analytics, enabling merchants to track transactions in real-time. DoorDash relies on Pinot to surface delivery trends and operational dashboards seamlessly within their platform. These are just a few of the use cases that showcase Pinot’s ability to deliver millisecond-latency queries and scalable real-time analytics directly within user-facing applications. Learn more about embedded analytics use cases here.