Operations - Rowbase

Overview

Operations are transformations you apply to datasets. They’re non-destructive - your original data is always preserved, and you can add, remove, or reorder operations at any time.

Operation Pipeline

Operations are applied in sequence, creating a transformation pipeline:

Click any operation in the pipeline to edit it. Drag to reorder. Changes are applied instantly.

AI Operations

Use AI

Generate AI-powered content based on other column data. Perfect for enrichment, classification, and content generation.

Input: product description
Output: AI-generated category, summary, or tags

Detect Headers

Use AI to detect column headers in messy data and rename columns automatically.

Before: _UNNAMED_0, _UNNAMED_1
After: product_name, price

Transform Operations

Filter Rows

Keep or exclude rows based on a condition expression.

Simple
Multiple Conditions

Filter by a single condition:

Field	Operator	Value
status	equals	active

Combine conditions with AND/OR:

status = "active"
AND created_at > "2024-01-01"
AND (plan = "pro" OR plan = "enterprise")

Available operators:

Text: equals, not equals, contains, starts with, ends with, is empty, is not empty
Number: equals, not equals, greater than, less than, between
Date: equals, before, after, between, is empty
Boolean: is true, is false

Select Columns

Keep only specified columns, discarding the rest.

Before: name, age, email, phone, address
After: name, email

Rename Columns

Change column names for clarity or consistency.

"col1" → "customer_name"
"col2" → "email_address"

Bulk rename multiple columns at once
Formulas automatically update to use new names

Drop Column

Remove columns you don’t need.

Remove single or multiple columns
Removing a column used in formulas will show a warning

Copy Column

Create a copy of a column with a new name.

Before: email
After: email, email_backup

Add Literal Column

Add a new column with a constant value.

New column: "status"
Value: "active"

Formula

Compute values using Excel-like formulas with column references.

New column: "full_name"
Formula: {{first_name}} + " " + {{last_name}}

See Formulas for the complete function reference.

Text Operations

Trim Whitespace

Remove leading and trailing whitespace from text values.

Before: "  hello  "
After: "hello"

Collapse Whitespace

Replace multiple consecutive spaces with a single space.

Before: "hello   world"
After: "hello world"

Normalize Case

Convert text to lowercase, uppercase, or title case.

Before: "HELLO"
After: "hello"

Strip Prefix / Suffix

Remove a specified prefix or suffix from text values.

Before: "PREFIX_value"
After: "value"

Replace Values

Find and replace text patterns within values.

Find: "NY"
Replace: "New York"
Column: state

Snake Case Names

Convert all column names to snake_case format.

Before: "Product Name", "First Name"
After: "product_name", "first_name"

Parsing Operations

Parse Number

Convert text to numeric values, handling various formats.

Before: "1,234.56"
After: 1234.56

Parse Boolean

Convert text like ‘yes’, ‘no’, ‘true’, ‘false’ to boolean values.

Before: "yes"
After: true

Parse Timestamp

Parse date/time strings into standardized timestamp format.

Before: "01/15/2024"
After: "2024-01-15T00:00:00Z"

Parse Money

Extract numeric amounts from currency strings.

Before: "$1,234.56"
After: 1234.56

Parse Percentage

Convert percentage strings to decimal values.

Before: "50%"
After: 0.5

Parse Quantity & Unit

Split values like ‘5 mg’ into separate quantity and unit columns.

Before: "5 mg"
After: 5, "mg" (two columns)

Null Handling

Empty to Null

Convert empty strings to null values.

Before: ""
After: null

Null to Default

Replace null values with a specified default value.

Before: null
After: "(default)"

Coalesce Fields

Use the first non-null value from multiple columns.

Columns: nickname, first_name, "Unknown"
Result: first non-null value

Merge Operations

Union

Combine rows from multiple tables into a single output.

Table A: 100 rows
Table B: 50 rows
Result: 150 rows (stacked)

Join

Merge two tables by matching rows on common keys.

Left table + Right table
Join on: customer_id
Result: Combined columns where keys match

Join types:

Left join (keep all left rows)
Inner join (only matching rows)
Full outer join (keep all rows)

Map Values

Replace values using a lookup table (e.g., codes to names).

Lookup: "US" → "United States"
Before: "US"
After: "United States"

Identity Operations

Generate Unique ID

Create a unique identifier for each row (like a fingerprint for your data).

New column: rb_id
Value: rb_abc123...

Apply Corrections

Apply manual data corrections and overrides from the corrections table.

Validation Operations

Require Fields

Ensure specified fields are present and not null in every row.

Unique Constraint

Verify that values in specified columns are unique across all rows.

Primary Key Constraint

Enforce primary key uniqueness and non-null constraints.

Validate Schema

Validate data types and structure against a defined schema.

Quarantine If

Quarantine rows that fail a custom formula-based validation rule.

Rule: {{price}} > 0
Result: Rows with zero or negative price are quarantined

Quarantined rows are moved to a separate table for review, not deleted.

Quality Flags

Flag rows that match specific quality issues:

Operation	Flags rows where…
Flag Negative	Numeric field contains negative values
Flag Zero	Numeric field contains zero values
Flag If Less Than	One column is less than another
Flag Invalid Units	Units not in allowed list
Flag Missing Units	Quantity has no associated unit

Operation History

Every operation is tracked in the history:

View when each operation was added
See who made changes (in team workspaces)
Undo/redo operations
Reset to any point in history

Best Practices

Order matters

Operations apply in sequence. Filter before validation to ensure you only validate relevant records.

Use descriptive names

Name your operations (e.g., “Remove inactive users”) so teammates understand the pipeline.

Preview before applying

Always check the preview to verify the operation does what you expect.

Use AI for complex tasks

When pattern-based operations aren’t enough, use AI operations to handle nuanced transformations.

​Overview

​Operation Pipeline

​AI Operations

​Use AI

​Detect Headers

​Transform Operations

​Filter Rows

​Select Columns

​Rename Columns

​Drop Column

​Copy Column

​Add Literal Column

​Formula

​Text Operations

​Trim Whitespace

​Collapse Whitespace

​Normalize Case

​Strip Prefix / Suffix

​Replace Values

​Snake Case Names

​Parsing Operations

​Parse Number

​Parse Boolean

​Parse Timestamp

​Parse Money

​Parse Percentage

​Parse Quantity & Unit

​Null Handling

​Empty to Null

​Null to Default

​Coalesce Fields

​Merge Operations

​Union

​Join

​Map Values

​Identity Operations

​Generate Unique ID

​Apply Corrections

​Validation Operations

​Require Fields

​Unique Constraint

​Primary Key Constraint

​Validate Schema

​Quarantine If

​Quality Flags

​Operation History

​Best Practices

Overview

Operation Pipeline

AI Operations

Use AI

Detect Headers

Transform Operations

Filter Rows

Select Columns

Rename Columns

Drop Column

Copy Column

Add Literal Column

Formula

Text Operations

Trim Whitespace

Collapse Whitespace

Normalize Case

Strip Prefix / Suffix

Replace Values

Snake Case Names

Parsing Operations

Parse Number

Parse Boolean

Parse Timestamp

Parse Money

Parse Percentage

Parse Quantity & Unit

Null Handling

Empty to Null

Null to Default

Coalesce Fields

Merge Operations

Union

Join

Map Values

Identity Operations

Generate Unique ID

Apply Corrections

Validation Operations

Require Fields

Unique Constraint

Primary Key Constraint

Validate Schema

Quarantine If

Quality Flags

Operation History

Best Practices