Clean, deduplicate, and prepare raw data online

Join, merge, and group datasets seamlessly. Filter columns, add calculated fields, and sort rows to transform messy datasets into clean, business-ready files with zero code.

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Reshape a single table

The everyday cleanup: fix headers, add fields, trim columns, drop repeats, and order rows.

Rename Columns

Standardize raw headers into clean, human-readable labels.

Turn cryptic headers like TOT_REV or col_3 into clear, report-ready labels. Header names update instantly while all underlying data values remain completely untouched.

How to use it

  1. Select the field name you want to edit.
  2. Enter your standardized header name.
  3. Click Apply to update your dataset schema.
Standardize headersMatch a target schemaPrep for BI & analytics

Tip. Renaming only modifies header rows. Your source data values remain 100% intact.

Add Columns

Enrich your dataset with calculated fields and new parameters.

Build new columns using expressions like revenue / units or now(). Easily compute margins, set static text, and format date-time fields across every row in your dataset.

How to use it

  1. Enter a name for your new field.
  2. Choose a type such as Expression, String, Date, Integer, or Boolean.
  3. See it computed on every row instantly.
Calculated fieldsCustom formulasDate parsingConditional flags

Tip. Expressions can reference any existing field in your file to compute custom calculations across all rows instantly.

Select Columns

Filter out unnecessary fields and reorder columns for clean exports.

Select which columns to carry forward and arrange them in your preferred sequence. Remove bloated fields and sensitive PII to create concise, shareable tables optimized for your target system.

How to use it

  1. Select a field name from your column list.
  2. Use the Up and Down controls to change the column order.
  3. Click Delete to strip unwanted fields, then hit Apply.
Trim wide exportsField reorderingStrip sensitive PIISchema optimization

Tip. Removing unneeded columns reduces file export sizes and prevents sensitive data from leaking into downstream reports.

Remove Duplicates

Eliminate duplicate rows and ensure accurate metric reporting.

Collapse exact repeat rows or deduplicate based on specific key fields. Prevent double-counting and maintain data integrity so your reporting totals remain completely accurate.

How to use it

  1. Select the specific columns to check for duplicate values.
  2. Uncheck fields that should be ignored during matching.
  3. Click Apply to retain the first record of each unique set.
Deduplicate datasetsClean CRM exportsFix repeated log rowsData integrity

Tip. Selecting specific key columns like Order ID or Email lets you catch near-duplicates even if other fields vary.

Sort Columns

Order rows by one or more key attributes.

Sort rows by one or multiple key fields across text, numbers, or dates. Set up secondary sort levels as tie-breakers to structure your records cleanly for analysis or export.

How to use it

  1. Select a field from the dropdown list.
  2. Choose your sort direction (e.g., Ascending A-Z or Descending).
  3. Add additional fields as tie-breakers and click Apply.
Multi-level sortingChronological orderTop-N reportingAlphabetize lists

Tip. Add secondary sort rules to keep related records neatly organized when primary field values match.

Summarize many rows into few

Turn cleaned records into the totals you actually report on.

Summarize Data

Summarize raw transaction records into clean, aggregated rollup tables.

Group datasets by key category fields and calculate summary metrics. Apply aggregation functions like Sum, Count, Average, First, Last, Minimum, and Maximum to transform granular line items into executive summary reports.

How to use it

  1. Select your grouping dimension and set the operator to Group By.
  2. Click + Add Field and assign operators like Sum, Count, or Average to target metrics.
  3. Toggle Exclude Nulls if necessary, then click Apply.
Category rollupsSummary tablesMetric aggregation

Tip. Combine a Count operator with a Sum operator on the same category to track both total transaction volume and financial totals in a single summary table.

Bring two files together

Enrich and extend your data with a second file.

Join Data

Enrich datasets by matching key columns across two files.

Combine related records from two tables using shared key fields. Execute Inner, Left, Right, or Full Outer joins to attach reference data, lookup tables, or secondary attributes without writing SQL or complex spreadsheet formulas.

How to use it

  1. Upload a second file, load a URL, or choose a saved dataset.
  2. Select your join type (Inner, Left, Right, or Full Join).
  3. Map matching key columns between both files and click Apply.
Flexible join typesRelational key mappingLookup table enrichmentZero-code SQL joins

Tip. Use a Left Join to retain every record from your primary table while pulling matching details from your secondary file.

Merge Data

Stack multiple datasets vertically into a single unified table.

Append rows from multiple files sharing similar structures. Union monthly exports, combine regional reports, and align matching fields to consolidate separate data streams into one continuous file.

How to use it

  1. Upload additional files, load via URL, or select saved datasets.
  2. Map source fields to target columns or uncheck the Keep box to drop a field.
  3. Click Apply to concatenate all rows into a single table.
Combine monthly exportsUnion datasetsConsolidate team filesVertical row stacking

Tip. Fields are matched by column name, so your source files do not need to have their headers in the exact same order.

Prep it once. Export to any format.

Transform raw files with 8 zero-code tools and export clean data to CSV, JSON, Excel, SQL, and 22+ formats in seconds.

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