Joule is a Low Code Platform for use case development. The platform brings simplicity to developing use cases by providing an expressive language to define processing pipelines using prebuilt and custom processors and data connectors.
Out-of-the-box Joule provides standard data connector implementations, and useful processors that enables you to start building and running use cases quickly.
This early release brings a number of new features, bug fixes, optimisations and general usability enhancements.
Key Features
Feature Engineering
Joule provides a feature engineering processor that enables users to define how features are to be created ready for predictive analytics use cases.
The processor generates for each declared feature field an engineered value. Two methods are supported; raw and compute values using custom expression and plugins. On completion a feature map is generated will all the required features and placed in the StreamEvent ready for the next processor in the pipeline.
To get you started OOTB plugins are provided for the following functional categories:
Scripting
Scaling
Transform
Example
feature engineering:
name:retailProfilingFeatures
versioned:true
features:
as values:
-location_code
-store_id
compute:
spend_ratio:
scripting:
macro:
expression:1-spend/avg_spend
language:js
variables:
avg_spend:133.78
age:
function:
age binning:
source field:date_of_birth
day:
function:
day-of-week transform:
source field:date
See documentation for further details.
Machine Learning & Auditing
Joule provides a PMML predictor processor to perform streaming predictions / scoring. The implementation leverages the JPMML open source library developed by Villu Ruusmann.
Example
pmml predictor:
name:irisScorer
model filename:/hom/joule/models/pmml/iris_rf.pmml
response field:flowerPrediction
audit configuration:
target schema:ml_audit
queue capacity:5000
flush frequency:5Auditing
An optional configuration provide the ability to audit predications to enable model retraining, feature and prediction drift management, model observability, and any local business governance requirements.
The configuration will dynamically create an in-memory database table, using the process name as the target table, and rest endpoints to enable direct access and export functions.
With these new features Joule can now provide stream based advanced analytics using PMML models within Docker containers. An example is illustrated below.
See documentation for further details.
SQL Query API
Joule embeds DuckDB, an in-memory database, in to the runtime process. The solution is ideal for supporting custom processor logic using various methods such as:
Hosting and accessing custom reference data
Scratchpad for stateful processing
Ad-hoc custom complex queries
Capture and exporting streaming events
See documentation for further details.
Websocket Support
Joule now supports publishing events on to a Websocket publisher. Events are serialised as Json.
Example
websocketPublisher:
pathOverride:/joule/websocket/streamSee documentation for further details.
Project Templates
To kick start transport and processor custom development a projects template project is provided. The project can be found on this link.
Documentation
As an ongoing effort to provide good quality documentation for the platform this release has reorganised, added new sections, more details and much more. The online documentation can be found here.
Getting started
To get started, download the following resources to prepare your environment and work through the provided documentation. Feel free to reach out with questions.
We’re Here to Help
Feedback is most welcome including thoughts on how to improve and extend Joule and ideas on exciting use cases.
You’re in this with the entire FractalWorks community, who’s openly sharing ideas, and best practices and helping each other on our Community Forum. Feel free to join us there! And if you have any further questions on how to become a partner or customer of FractalWorks, do not hesitate to engage with us, we will be happy to talk about your needs.