Joule Release 1.3.0
Joule cluster deployment with new transports and platform enhancements
Real-Time Operational Intelligence
Transform raw data into actionable operational insights instantly.
Embed analytics directly in streaming pipelines
for real-time metrics, complex calculations, and predictive scoring with no extra infrastructure required.
Build, deploy, and validate
analytics with custom expressions and ML inferencing to power smarter, faster business decisions.
Analytic features
Built to handle the complexity of real-time analytics, Joule empowers businesses to act smarter and faster while reducing operational costs.
Compute continuous metrics within in-memory SQL engine for instant analysis and triggering.
Embed predictive scoring using ML models enabling real-time decision making.
Apply custom analytics withJS/Python expressions, event windows and Joule SDK.
Real-time geospatial intelligence with entity tracking and geofence occupancy monitoring.
Staying informed with near real-time business metrics allows organisations to make swift, decisive actions that enhance success.
Compute metrics in near real-time over event micro-batches, enabling KPIs, alerts, and downstream analytics.
Seamlessly combine metrics with custom analytics and enrichment data to generate actionable insights in real time
Built on a high-performance in-memory SQL-compliant database with configurable execution and storage policies for complete control.
// Metrics engine quote pipeline
processing unit:
metrics engine:
runtime policy:
frequency: 1
startup delay: 1
time unit: MINUTES
foreach metric compute:
metrics:
- name: BidMovingAverage
metric key: symbol
table definition: bid_moving_averages (symbol VARCHAR, avg_bid_min FLOAT, avg_bid_avg FLOAT, avg_bid_max FLOAT, createdTimestamp TIMESTAMP)
query: SELECT symbol, MIN(bid) AS 'avg_bid_min', AVG(bid) AS 'avg_bid_avg', MAX(bid) AS 'avg_bid_max' FROM quotes.nasdaq WHERE ingestTime >= epoch_ms(date_trunc('minutes', now() - INTERVAL 3 MINUTES)) AND ingestTime <= epoch_ms(now()) GROUP BY symbol ORDER BY 1;
truncate on start: true
compaction policy:
frequency: 8
time unit: HOURS
pipeline:
- tap:
target schema: quotes
flush frequency: 5
index:
unique: false
fields:
- symbol
emit:
select: symbol, BidMovingAverage.avg_bid_max;WHERE symbol=${symbol} 'avg_bid_max'
group by:
- symbol Joule embeds a SQL-compliant metrics engine to compute and store metrics using scheduled user-defined SQL expressions over event micro-batches producing KPIs, alerts, and predictive insights for downstream analytics.
Evaluates event feature vectors in near real time using JPMML, enabling low-latency predictive scoring directly within streaming pipelines.
Joule provides a feature engineering processor that enables users to define how features are to be created ready for predictive analytics use cases.
The predictor processor can be configured to load and dynamically refresh a model from a configured data storage platform.
Support explainability, detect drift, and manage retraining through model feature and prediction auditing.
Joule ships with a rich set of built-in capabilities, including statistical window functions, machine learning normalization routines, bloom filters, and many more ready to use in your streaming pipelines.
Joule enables users to harness existing analytical resources, define expressions, integrate reference data, and develop custom analytic plugins for complex calculations.
Use stateful analytics using preconfigured memory of previous result, event windows or through using the last emitted event.
Perform real-time geospatial tracking of entities such as vehicles and assets of interest using geotagged events.
Track locations of interest in real time by identifying entity entry, dwell time, and exit events within defined geofences.
Resolve IP addresses to geolocation data to analyze web reach, DDoS activity, and traffic distribution patterns.
Joule gives you the tools to innovate without the wait. Start building real-time streaming analytics solutions today.
Joule cluster deployment with new transports and platform enhancements
The Joule + DuckDB integration unlocks advanced streaming analytics by embedding a high-performance, in-memory database directly into the Joule runtime.
Enriching events with contextual data is key for advanced streaming use cases
Joule now ships with real-time inferencing enabling advanced use cases