> For the complete documentation index, see [llms.txt](https://docs.fractalworks.io/joule/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fractalworks.io/joule/components/processors/enrichment.md).

# Enrichment

## Overview

Enrichment is a common practice across industries and Joule makes it simpler.

In advanced streaming scenarios, it's often necessary to add extra contextual data to **improve processing** **accuracy** and **decision-making**.

By using **low-latency data stores**, streaming events can be quickly enriched with up-to-date information, ensuring **fast and responsive** data retrieval.

The key concepts and the anatomy of enrichment DSL will deep dive in the concept of enrichment.

{% content-ref url="/pages/P2U7YaAGfx26YanvGGI6" %}
[Key concepts](/joule/components/processors/enrichment/key-concepts.md)
{% endcontent-ref %}

{% content-ref url="/pages/TORLKFpfwBTmHPCqYskG" %}
[Anatomy of enrichment DSL](/joule/components/processors/enrichment/key-concepts/anatomy-of-enrichment-dsl.md)
{% endcontent-ref %}

## Use cases

Here are some use cases of how it can be applied in stream processing:

1. <mark style="color:green;">**Customer data**</mark>

   Adding information such as data bundle plans or credit ratings.
2. <mark style="color:green;">**Company metrics**</mark>

   Enriching streams with market ratings or previous day stock sensitivities.
3. <mark style="color:green;">**Technical operations**</mark>

   Including data like cell tower signal coverage or machine utilisation limits.
4. <mark style="color:green;">**Geofencing**</mark>

   Enriching streams with static geofences for places of interest.
5. <mark style="color:green;">**Dynamic triggers**</mark>

   Incorporating real-time business triggers like SLAs or stock price levels.
6. <mark style="color:green;">**Domain-specific metrics**</mark>

   Adding general KPIs or metrics for domain-level insights.

## Available enrichment options

Contextual data, usually stored in data marts or warehouses changes slowly. Integrating it into a high-throughput streaming platform **can cause processing delays**.

These processing delays are often due to the I/O overhead from the network and storage layers. To tackle these challenges, Joule offers a set of optimised solutions. Following enrichers are available.

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><mark style="color:orange;"><strong>Metrics</strong></mark></td><td>Enrich events with pre-calculated metrics to facilitate further pipeline processing</td><td></td><td><a href="/pages/1LlQfAGSJ6fbBCg60WU1">/pages/1LlQfAGSJ6fbBCg60WU1</a></td></tr><tr><td><mark style="color:orange;"><strong>Dynamic contextual data</strong></mark></td><td>Use fast, connected data stores to quickly enrich in-motion events with up-to-date context</td><td></td><td><a href="/pages/FNkKaC5bJUKw4pbGxQJO">/pages/FNkKaC5bJUKw4pbGxQJO</a></td></tr><tr><td><mark style="color:orange;"><strong>Static contextual data</strong></mark></td><td>Enrich events with essential slow changing data from Joule’s in-memory database.</td><td></td><td><a href="/pages/ETGwHElBexCVHhGZU3E8">/pages/ETGwHElBexCVHhGZU3E8</a></td></tr></tbody></table>