Why Enrich processor in Elasticsearch? - Purpose & Use Cases

The Big Idea

What if your data could automatically fill in missing details without you lifting a finger?

The Scenario

Imagine you have a big list of customer orders and another list with detailed customer information. You want to add the customer details to each order manually by looking up each customer one by one.

The Problem

Doing this by hand or with simple scripts is slow and mistakes happen easily. You might miss some customers or add wrong details because the lists are large and not connected automatically.

The Solution

The Enrich processor in Elasticsearch automatically adds extra data from one index to documents in another during indexing. It saves time and avoids errors by doing the lookups and merges for you.

Before vs After

✗ Before

for order in orders:
    customer = find_customer(order.customer_id)
    order.details = customer.details

✓ After

PUT _ingest/pipeline/enrich_pipeline
{
  "processors": [
    {
      "enrich": {
        "policy_name": "customer_policy",
        "field": "customer_id",
        "target_field": "customer_details"
      }
    }
  ]
}

What It Enables

You can enrich your data automatically and reliably during indexing, making searches smarter and faster without extra manual work.

Real Life Example

A company indexes sales orders and uses the Enrich processor to add customer loyalty status from another index, so they can quickly find orders from VIP customers.

Key Takeaways

Manual data merging is slow and error-prone.

Enrich processor automates adding related data during indexing.

This makes your search data richer and more useful instantly.