Pricing

from $2.00 / 1,000 results

Metaworld Data Cleaner

Messy CSV in — validated, deduplicated, typed records out, with a data-quality report naming every rejected row and why.

Pricing

from $2.00 / 1,000 results

Rating

0.0

(0)

Developer

Metaworld Systems

Actor stats

Bookmarked

Total users

Monthly active users

3 hours ago

Last modified

What it does

Normalizes every field — trims whitespace, strips control characters, and standardizes dates (YYYY-MM-DD), phone numbers ((727) 450-9666), and currency (1234.50) based on the column name.
Validates rows against required columns you choose — anything missing a required value is rejected, not silently dropped.
Deduplicates on a key column, with optional latest-wins survivorship using a timestamp column.
Reports — a quality report is written to the key-value store (QUALITY_REPORT) listing row counts and every rejected row with the reason.

Input

Field	Description
`csvUrl`	Public URL of a CSV to clean.
`csvText`	Or paste raw CSV (with a header row).
`requiredColumns`	Rows missing any of these are rejected.
`dedupeKey`	Column used to detect duplicates (optional).
`timestampColumn`	When deduping, keep the newest row by this column.

Provide either csvUrl or csvText.

Output

Dataset — one item per cleaned, validated, deduplicated row (export as CSV, JSON, or Excel).
Key-value store QUALITY_REPORT — rows_in, rows_cleaned, rows_rejected, and the full list of rejected rows with reasons.

Example

Input csvText:

order_id,customer,order_date,amount,phone
1001, Acme Co ,2026-1-3,$1,299.00,7274509666
1001,Acme Co,01/03/2026,1299,(727) 450-9666
1002,,2026-02-15,89.5,555 123 4567

With requiredColumns=["order_id","customer"], dedupeKey="order_id", timestampColumn="order_date":

Row 1002 is rejected (missing customer).
The two 1001 rows collapse to one (latest-wins).
Dates become 2026-01-03, amount becomes 1299.00, phone becomes (727) 450-9666.

Why this one

We don't sell you the happy path. The value is the report that tells you exactly which rows failed and why — so you fix your data instead of finding out downstream. Same pattern we run in production for our own operations.

All processing happens inside the Actor run; nothing is stored or shared beyond your run's own storages.

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