Filter Data

Filter Data applies conditions to your dataset and returns only the rows that match your criteria. Essential for extracting specific data subsets, analyzing targeted segments, or preparing focused datasets for further processing.

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When to Use It

• Filter high-performing products based on sales metrics
• Extract data for specific date ranges or regions
• Find records that meet multiple criteria simultaneously
• Clean datasets by removing unwanted or irrelevant rows

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Inputs

Field	Type	Required	Description
Data to Filter	Data	Yes	The dataset you want to filter (from previous steps)
Conditions	Conditions	Yes	Filter criteria based on column values and conditions

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Outputs

Output	Description
Filtered Data	Dataset containing only rows that meet your conditions

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Credit Cost

Free to use - no credits required.

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How It Works

Filter Data examines each row in your dataset and applies your conditions. Only rows that meet ALL your conditions are included in the filtered output (AND logic between multiple conditions). Example: Original Data:

campaign_name	status	cost	clicks
Summer Sale	Active	150	1200
Winter Promo	Paused	80	500
Black Friday	Active	300	2500
Test Campaign	Active	20	100

Filter Conditions:

• status equals “Active”
• cost greater than 100

Filtered Result:

campaign_name	status	cost	clicks
Summer Sale	Active	150	1200
Black Friday	Active	300	2500

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Available Filter Conditions

Condition	Description	Example Use Case
EQUALS	Exact match	Status equals “Active”
NOT_EQUALS	Does not match exactly	Status not equals “Paused”
CONTAINS	Contains the text	Campaign Name contains “Sale”
DOES_NOT_CONTAIN	Does not contain the text	Campaign Name does not contain “Test”
STARTS_WITH	Begins with the text	Campaign Name starts with “2024”
ENDS_WITH	Ends with the text	Campaign Name ends with “Mobile”
GREATER_THAN	Numeric comparison	Cost greater than 100
LESS_THAN	Numeric comparison	Clicks less than 1000
GREATER_THAN_OR_EQUAL	Numeric comparison	Budget greater than or equal to 500
LESS_THAN_OR_EQUAL	Numeric comparison	Cost less than or equal to 200
REGEXP_MATCH	Regular expression match	Campaign Name matches pattern
NOT_REGEXP_MATCH	Does not match regex	Campaign Name does not match pattern
IN_LIST	Value is in a list	Status in “Active,Enabled,Running”
NOT_IN_LIST	Value is not in a list	Status not in “Deleted,Removed”
IS_NULL	Field is empty	Notes is null
IS_NOT_NULL	Field has any value	Notes is not null

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Filter Examples

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Example 1: Active High-Budget Campaigns

Conditions:

• Field: “status” | Condition: “EQUALS” | Value: “Active”
• Field: “budget” | Condition: “GREATER_THAN” | Value: “1000”

Result: Only active campaigns with budget over $1000

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Example 2: Performance Analysis

Conditions:

• Field: “clicks” | Condition: “GREATER_THAN” | Value: “500”
• Field: “cost_per_click” | Condition: “LESS_THAN” | Value: “2.00”

Result: High-click campaigns with low cost per click

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Example 3: Campaign Name Filtering

Conditions:

• Field: “campaign_name” | Condition: “CONTAINS” | Value: “2024”
• Field: “campaign_name” | Condition: “DOES_NOT_CONTAIN” | Value: “Test”

Result: 2024 campaigns excluding test campaigns

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Example 4: Multi-Status Filtering

Conditions:

• Field: “status” | Condition: “IN_LIST” | Value: “Active,Enabled,Running”

Result: Campaigns with any of the specified active statuses

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Tips

Field Names:

• Use exact column names from your data (case-sensitive)
• Check your data structure first to confirm field names
• Column names should match exactly what appears in your dataset headers

Condition Logic:

• All conditions must be met (AND logic) for a row to be included
• Use multiple Filter Data nodes for OR logic between different condition sets
• Test with a small dataset first to verify your conditions work as expected

Data Types:

• Use numeric comparisons (GREATER_THAN, LESS_THAN) for numbers
• Use text comparisons (CONTAINS, EQUALS) for text fields
• Date comparisons work with properly formatted date fields

Performance:

• Filter early in your workflow to reduce data size for subsequent nodes
• Use specific conditions to avoid processing unnecessary data
• Consider the order of conditions - put most selective conditions first

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FAQ

What happens if no rows meet my conditions?

You’ll get an empty dataset (no rows), but the column structure remains intact. This won’t cause an error, but subsequent nodes may need to handle empty data appropriately.

Can I use OR logic between conditions?

No, all conditions use AND logic (all must be met). For OR logic, use multiple Filter Data nodes or separate your data into different paths and merge the results later.

How do I know what field names to use?

Use the exact column names from your data source. You can check these by running a small test or looking at the data preview from your previous workflow steps.

What if my field name has spaces or special characters?

Use the exact name including spaces and special characters. For example, if your column is called “Campaign Name”, use “Campaign Name” in your filter condition.

Can I filter by calculated values or formulas?

No, you can only filter by actual column values in your data. If you need to filter by calculated values, use other nodes to create those calculated columns first, then filter.

What happens if I reference a field that doesn't exist?

The filter condition will be ignored and you’ll get your original unfiltered data. Make sure field names match exactly what’s in your dataset.

How do I filter for empty or null values?

Use “IS_NULL” to find empty fields or “IS_NOT_NULL” to find fields with any value. This is useful for data quality checks and cleaning.

Can I use regular expressions for complex text matching?

Yes, use “REGEXP_MATCH” for pattern matching. For example, to find campaigns with numbers: field “campaign_name”, condition “REGEXP_MATCH”, value ”.\d+.”.