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Joiner overview

Joiner

Joiner helps you combine Records from two datasets using matching keys.

Use Joiner when the information you need is split across Files and the next step depends on bringing related Fields together in one result. Instead of copying Fields manually or relying on one-off spreadsheet formulas, you can create a repeatable Configuration that defines how the datasets should match and which Fields should appear in the output.

What Joiner is for​

Joiner is a good fit when you need to:

  • add reference data to a transaction file
  • combine customer, account, product, or location details from another dataset
  • compare whether Records in one File have a match in another File
  • prepare a merged output for reporting, upload, review, or another WebHammers step
  • make recurring merge logic easier to repeat and explain

What a Joiner configuration does​

A Joiner configuration defines how two datasets relate to each other.

A strong configuration usually includes:

  • the primary input dataset
  • the lookup or secondary dataset
  • the key Fields used to match Records
  • the Fields to include from each dataset
  • the join behavior for matched and unmatched Records
  • a review plan for Records that do not match as expected

The goal is to make the relationship between the files explicit and repeatable.

Why teams use Joiner​

Teams often use Joiner when important context lives outside the main file.

Common examples include adding customer names to account activity, attaching product categories to sales lines, adding owner details to cases, or enriching a list with region, department, or status Fields maintained somewhere else.

Joiner helps reduce manual spreadsheet work and makes the merge logic easier to review later.

Typical Joiner workflow​

A common workflow looks like this:

  1. identify the two datasets that need to be combined
  2. confirm the key Fields that should match
  3. decide which Fields should appear in the output
  4. create or select a Joiner Configuration
  5. test the Configuration on representative Records
  6. review matched and unmatched Records
  7. reuse the Configuration for similar Files in the future

What makes a Joiner setup effective​

The best Joiner configurations are clear about the matching relationship.

A strong setup usually has:

  • reliable key Fields in both datasets
  • consistent formatting in the match Fields
  • a narrow output that includes only useful Fields
  • clear handling for unmatched Records
  • a name that explains the business purpose

If the match Fields are messy or inconsistent, clean or validate them first so the join result is easier to review.

When Joiner is not the best starting point​

Joiner is not usually the first Tool to use when:

  • values need to be standardized before matching
  • duplicates must be resolved before one-to-one matching will make sense
  • the main task is only to keep or exclude Records
  • the goal is to split one file into several outputs
  • the desired result is a statistical summary rather than a merged record set

Continue with these pages:

  • When to use Joiner
  • Create a Joiner configuration
  • Run Joiner
  • Joiner examples
  • Joiner FAQ