Clean, reshape, and standardize data without writing custom ETL pipelines. The
data_transform task type
handles format conversion, field mapping, deduplication, and normalization at any scale.
Convert CSV → JSON, XML → CSV, YAML → JSON, or any custom schema mapping.
Fix typos, standardize date formats, normalize phone numbers and addresses.
Map fields between different data models or API schemas automatically.
Group, sum, average, and pivot data across thousands of records in one task.
Auto-tag and categorize records based on content, rules, or ML classifiers.
Identify and remove duplicate records using fuzzy matching across datasets.
curl -X POST https://api.crowdsorcerer.dev/v1/tasks \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"type": "data_transform",
"input": {
"data": [
{ "Name": "john doe", "Phone": "555-1234", "Date": "01/15/24" },
{ "Name": "Jane Smith", "Phone": "(555) 5678", "Date": "2024-01-16" }
],
"transformations": [
{ "op": "normalize_name", "field": "Name" },
{ "op": "format_phone", "field": "Phone", "format": "E164" },
{ "op": "parse_date", "field": "Date", "output_format": "ISO8601" }
]
}
}'500 free credits on signup. No credit card required.