Metrics engine
In-memory SQL compliant metrics engine for advance analytics solutions
Overview
Staying informed with near real-time business metrics allows organisations to make swift, decisive actions that enhance success. Joule’s SQL-compliant metrics engine facilitates this by generating real-time analytics and storing metrics through SQL expressions, supporting KPIs, alerting and predictive insights.
By providing a framework for proactive management, Joule enables organisations to focus on clear, measurable goals and make data-driven decisions. The engine computes metrics using event data and runtime policies, empowering users to analyse and react to data effectively as it streams.
Use cases
Here are some use cases of how metrics it can be applied in stream processing:
Business metrics monitoring Track intra-day business metrics such as number of orders placed in an hour.
Proactive service failure alerting Track key failure indicators to raise early failure alerting.
Data quality metrics Monitor data quality in near real-time.
ML feature engineering Use calculated metrics as feature vector components.
KPI analytics Apply metrics to form intra-day KPIs. For example Conversion rate uses the number of conversions and total audience are running metrics to form the result.
Key features
ANSI SQL compliant metrics calculation definition
Apply calculated metrics within processors
Pre-computed metrics import
Runtime policy
Metric management
Powered by the DuckDB In-memory column database
Example
The following example computes a single family of metrics BidMovingAverage
at set time intervals and saved to an in-memory standardQuoteAnalyticsStream.BidMovingAverage
SQL table.
The emit
definition performs a query lookup against the table and returns the avg_bid_max
for each matching symbol.
Available metric engine options
In the following articles you can further explore how to interact with the metrics engine.
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