Choose the right Tool
Start with the Tool that matches your actual task.
Quick decision guide
| If you want to... | Use this Tool |
|---|---|
| standardize messy values | Cleaner |
| keep only Records that match Rules | Filterer |
| remove repeated Records | Deduplicator |
| check whether values meet requirements | Validator |
| translate values from one form to another | Mapper |
| hide or mask sensitive values | Obfuscator |
| understand the shape and quality of a dataset | Data Profiler |
| combine Records from two datasets | Joiner |
| divide one dataset into separate outputs | Splitter |
| calculate counts, totals, averages, or distributions | Statistics |
Common examples
I have inconsistent values in the same Field
Use Cleaner.
I need only a subset of the File
Use Filterer.
I suspect the File contains repeated Records or repeated entities
Use Deduplicator.
I need to check whether required Fields or allowed values are correct
Use Validator.
I need to convert codes, labels, or categories into another format
Use Mapper.
I need to reduce exposure of sensitive values before sharing a File
Use Obfuscator.
Obfuscator can mask, randomize, or replace selected Fields while keeping the dataset structure available for review.
I need a quick picture of completeness, distributions, and data quality signals
Use Data Profiler.
I need to combine Fields or Records from two datasets
Use Joiner.
I need to separate one dataset into multiple files or groups
Use Splitter.
I need numeric summaries, counts, totals, averages, or distribution insights
Use Statistics.
When one workflow may use more than one Tool
A real-world process may involve more than one step. For example:
- profile a dataset before doing anything else
- clean inconsistent values
- validate important Fields
- join related data from another File
- split the result into separate outputs
- calculate summary statistics
- obfuscate sensitive Fields before distribution
Tip for new users
When you are uncertain, start with Data Profiler or a small test Run in the Tool you think is most likely to fit. Reviewing a small output early usually clarifies the next step.