The next generation low code use case platform

Introduction

Joule is designed using an event-based stream processing architecture shaped by years of real-world experience designing and deploying distributed real-time event platforms. This practical foundation has led to a modular, extensible design with minimal external dependencies, purposefully designed to deliver only what each use case requires, with maximum efficiency and minimal overhead.

Joule achieves this through a well-defined domain-specific language (DSL), reusable event processors, out-of-the-box integrations with key third-party systems, and extensible platform toolkits.

Key features

Low code DSL to easily define use cases
Event processors covering encryption, filtering, enrichment, prediction, geospatial, rules engine, change data capture, and more
Pluggable reference data using Apache Geode distributed cache solution OOTB implementation
OOTB key product integrations for Kafka, RabbitMQ, Mosquitto, Minio, Drools, InfluxDB, and more
Java SDK for custom processors and transports
Flexible deployment model using either Docker containers or bare metal installations
Low latency high throughput stream processing environment.

Use cases

The Joule DSL is used to define use cases. A use case definition is composed of event sources, processing pipeline, event field projection, output format and transports. The platform is packaged with a suite of reusable processors, formatters, and transport integrations to accelerate use case development. Joule also ships with a Java SDK to support custom codec, processor and transport development.

Since reference data is typically a key requirement for any valuable stateless event processing an OOTB distributed in-memory cache solution is provided. The solution consists of local and distributed key value caching including:

Get Initial Image at process startup which primes both the local and distributed caching cluster; remove I/O overhead during critical event processing paths
Simple key and OQL query interface
Postgres integration for cache misses and offline data persistence

The packaged solution is built using Apache Geode as the distributed caching platform. For further details feel free to contact us.

An additional benefit of the Joule architecture is the ability to form complex solutions by deploying a use case DAG (lightweight microservices architecture), through the use of a messaging platform. Although this is somewhat obvious its worth pointing as it promotes use case reuse, modularity and testability.

Example complex use case

The example above represents a simple use case DAG where processing could be split across environments due to compliance requirements, events initiate within the DMZ environment. Each orange box is an independent use case process. Process chaining is achieved through the application of event subscription, Joule applies the PubSub model to all processing. The distributed cache provides the required reference data (i.e. opt-in and opt-out, enrichment etc,.).

Getting started

We have tailored a tutorial for anyone who wants to learn how to use Joule. The tutorial covers how to set up your environment, walk through building a use case and the deploy process.

The tutorial can be found here within the Joule documentation.