Data Transformation on Autopilot

Map fields, filter rows, format prices, and strip HTML. Combine multiple sources into one feed. Build if/then/otherwise rules visually, preview the output on real data, and run it on schedule. No code required.

14 day free trial • No credit card required
Input product_feed.csv · 12,400 rows
When
stock is greater than 0
Then
Set availability to "in stock"
Otherwise
Skip row
Map Value
size: "XL" → "Extra Large"
Output 9,614 rows • 2 rules applied

Transformation capabilities

Field mapping
Row filtering
Value formatting
HTML sanitization
Concatenation
Find & replace
Skip row
Lookup tables

Manual transformations are fragile

Marketing needs "Sale" appended to titles during promotions. Finance needs prices formatted differently. Your product manager wants HTML stripped from descriptions. Today, someone does this by hand in a spreadsheet. And they make mistakes.

  • Spreadsheet formulas break silently

    One extra column or missing row shifts everything. You won't notice until the feed is already delivered wrong.

  • Inconsistent formatting across sources

    Prices in different formats, HTML in text fields, mismatched categories, "XL" vs "Extra Large". Every source has its own quirks.

  • Developer bottleneck

    Every feed change means a developer ticket. A new column, a format tweak, a value mapping. The feedback loop is slow, expensive, and frustrating.

  • Scaling is painful

    One feed is manageable. Ten feeds across five markets with different formats, languages, and channel requirements? That's a full time job nobody signed up for.

transform_v3_FINAL_final.xlsx
BROKEN
SOLVED

Feed Panda Rules

7 rules • Priority ordered • Live preview

Field mapping 12 fields mapped
Rows filtered 558 skipped
Values formatted 3 rules
Lookup mappings 42 values mapped

Transform data in 3 steps

From messy source data to clean, structured output.

1

Map columns

Map source columns to output columns visually. Rename product_title to title, reorder with drag and drop, and set defaults for missing data.

2

Build rules

Add conditions (when), actions (then), and fallbacks (otherwise). Chain rules in priority order. Use operators like contains, starts with, greater than, pattern matching, and more.

3

Preview & run

Test rules against real data in a live preview. See how every transformation affects actual rows before saving. Then run on schedule or on demand.

Transformation features

A complete toolkit for shaping your data exactly how you need it.

Visual rule builder

Build if/then/otherwise logic visually. Each rule has conditions (when), actions (then), and fallbacks (otherwise). Drag to reorder priority. No code, no developer tickets.

When
Status equals "Active" AND stock > 0
Then
Set Priority to "High"
Otherwise
Set Priority to "Low"

Multiple source feeds

Combine data from multiple sources into one feed. Merge your product catalog from S3, pricing from FTP, and stock from Google Drive into a single unified output.

Row filtering

Skip products that are out of stock, exclude categories, or keep only rows that match. Equals, contains, starts with, greater than, empty fields, pattern matching, and more.

Actions & formatting

Anything you'd do to a field in a spreadsheet, done automatically on every row. Format prices, strip HTML from descriptions, map "XL" to "Extra Large", concatenate brand and title, or fill missing values with a default. All visual, no code.

ACTIONS
price
19.9 € 19,90
title
<b>Widget</b> Widget
size
XL Extra Large
display_name
brand + title Acme Widget

Reusable templates

Save rule sets as templates and apply them across multiple feeds. Update once, propagate everywhere. No more rebuilding the same transformations.

Live preview

Test your rules against real data before saving. See exactly how each transformation affects actual records without touching production output.

Lookup tables

Turn messy source values into the ones you actually want. Map "XL" to "Extra Large", vendor codes to category names, or any list of old to new values. Update the table once and every feed using it gets the fix.

Clean data, zero manual work

Build your transformations once. Let them run on every sync. Spend your time on strategy, not spreadsheet surgery.

Start free trial
14 day free trial No credit card required

Data transformation FAQ

Do I need coding skills to transform data?
No. Feed Panda provides a visual rule engine where you build transformations by selecting fields, choosing operations, and setting values. No code required. Conditions, actions, and preview are all visual.
What kind of transformations can I apply?
Set values, copy fields, concatenate columns, map values with lookup tables, find and replace text, trim whitespace, uppercase/lowercase, append/prepend text, strip HTML, format prices, truncate length, set defaults for empty fields, and skip entire rows based on conditions.
How do conditions work in rules?
Conditions use operators like equals, not equals, contains, not contains, starts with, ends with, is empty, is not empty, greater than, less than, and pattern matching. Combine multiple conditions with AND logic for precise targeting.
What is If/Then/Otherwise logic?
Each rule supports three branches: When (conditions that must be true), Then (actions to run when matched), and Otherwise (actions to run when not matched). One rule handles both cases, so you don't have to build two rules with opposite conditions.
Can I preview changes before they go live?
Yes. The rule editor includes a live preview that shows how your transformations affect actual data rows before you save or run the feed. Test against live data without touching production.
What are templates and how do they work?
Templates are reusable rule sets. Build a transformation once, save it as a template, and apply it across multiple feeds. When you update the template, every feed using it gets the change.
Can I control the order rules execute?
Yes. Rules execute in priority order. Drag and drop to reorder them. A "Set category" rule can run before a "Format category" rule, giving you full control over the transformation pipeline.