Continuous metrics
Real-time metrics provide the ability to drive advance insights and use cases
What will we learn on this article?
This article explains continuous metrics and how they enhance advanced use cases by integrating live metrics within stream processing.
You will learn how Joule uses DuckDB to generate, store, and manage these metrics, with examples like business dashboards and dynamic customer promotions.
What are continuous metrics?
Combining live metrics within stream processing context provides a powerful tool to build advance use cases.
Functions such as:
ML model predictions
Analytic calculations
Complex rule alerting logic
Filtering can leverage calculated metrics within the stream processing context.
Continuous metrics are generated on a scheduled cycle using captured streaming events. These are stored in-memory for any stream process to use.
Stream metrics should not be confused with streaming analytics. Streaming analytics will perform insight creation, pattern detection, generate next best action or alert based upon a condition.
How does Joule provide this feature?
Joule embeds an in-memory high performance analytics database, DuckDB, to provide the core analytical function.
The necessary supporting capabilities are provided with the following additional functions:
Scheduled metric generation
Table management
Metric priming and export
Data access API's
Event capture
Use case examples
The following use cases apply metrics in slightly different methods.
Business dashboards calculates and distributes whereas
Dynamic customer promotions adjusts customer purchase promotions based upon general buyer activity and live market conditions.
Business dashboards
Understanding intraday business activity is essential for various functions in today's competitive market.
By leveraging analytical insights and live metrics, businesses can shift from being reactive to applying proactive measures focused on the next best action.
Metrics play a crucial role in making this shift possible.
Dynamic customer promotions
Dynamically adjusting promotions based on market conditions, inventory levels, and customer buying behaviour can significantly impact product pricing.
By combining in-motion metrics with customer purchase history and intent, tailored promotions can be offered to customers.
Real-time metrics create a consistent approach for these scenarios by collecting and calculating key variables within a specific time frame.
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