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Splitter overview

Splitter

Splitter helps you divide one dataset into multiple outputs based on rules.

Use Splitter when one incoming File contains Records that need to be separated by category, value, condition, audience, or downstream destination. Instead of manually filtering and saving several copies, you can create a repeatable Configuration that routes Records into the right outputs.

What Splitter is for​

Splitter is a good fit when you need to:

  • create separate files for different teams, regions, owners, or categories
  • separate records by status, type, date range, or business condition
  • route exception records away from standard records
  • prepare multiple downstream uploads from one source file
  • make recurring file separation more consistent and reviewable

What a Splitter configuration does​

A Splitter Configuration defines how Records from one input should be assigned to outputs.

A strong configuration usually includes:

  • the source dataset
  • the Fields that drive the split
  • the Rules for each output group
  • names that make each output easy to understand
  • handling for Records that do not match any expected group
  • a review plan for counts and sample Records

Why teams use Splitter​

Teams often receive one broad file that needs to become several focused outputs.

Splitter helps reduce repetitive spreadsheet work, keeps routing rules consistent, and makes it easier to explain why each record went where it did.

Typical Splitter workflow​

A common workflow looks like this:

  1. identify the input dataset
  2. decide the output groups that should be created
  3. choose the Fields and Rules that assign Records to each group
  4. create or select a Splitter Configuration
  5. test with representative examples
  6. review Record counts and sample Records in each output
  7. reuse the Configuration for similar Files in the future

What makes a Splitter setup effective​

The best Splitter configurations have clear routing logic.

A strong setup usually has:

  • output groups with a clear purpose
  • rules that do not overlap unexpectedly
  • explicit handling for records that do not fit a group
  • names that make outputs easy to identify
  • a review process for output counts and edge cases

When Splitter is not the best starting point​

Splitter is not usually the first Tool to use when:

  • the source values need to be cleaned before routing will be reliable
  • duplicate records need to be resolved first
  • the main task is combining two datasets
  • the main task is only to keep one subset
  • the goal is a summary report rather than separate record-level outputs

Continue with these pages:

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