Data Table

Save and query structured data persistently across workflow executions

Overview

Data Table provides persistent tabular storage within the workflow platform. It supports typed columns (string, number, boolean, date), CRUD operations on rows, table management, conditional pass-through (row exists/not exists), filtering with multiple condition types, sorting, pagination, and bulk insert optimization. System columns (id, createdAt, updatedAt) are added automatically. This is an internal storage mechanism — no external API or credentials required.

Category: Core Nodes
Tool Name: data_table
Version: 1

Appearance: Icon: lucide-Database | Color: #FF6D5A

Node Type

Action — processes input items and produces output

Input / Output

Direction	Port(s)
Input	Input
Output	Output, Error

Credentials

This tool does not require any credentials.

Resources

Resource	Value
Row	row
Table	table

Operations

Rows (resource is row):

Operation	Value	Description
Delete	deleteRows	Delete row(s)
Get	get	Get row(s)
If Row Exists	rowExists	Match input items that are in the data table
If Row Does Not Exist	rowNotExists	Match input items that are not in the data table
Insert	insert	Insert a new row
Update	update	Update row(s) matching certain fields
Upsert	upsert	Update row(s), or insert if there is no match

Tables (resource is table):

Operation	Value	Description
Create	create	Create a new data table
Delete	delete	Delete a data table
List	list	List all data tables
Update	update	Update a data table name

Parameters

Row: Insert

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Column Mapping Mode	options	No	defineBelow	Whether to map input item fields automatically or define column values explicitly. Auto-map copies input JSON and strips system columns (id, createdAt, updatedAt).
				Options: autoMapInputData, defineBelow
Column Values	json	No	{}	JSON object with column names as keys and values to set. Example: {“name”: “John”, “age”: 30, “active”: true}. (shown when Column Mapping Mode is defineBelow)
Optimize Bulk	boolean	No	false	Whether to improve bulk insert performance by not returning inserted data (returns count only).

Row: Get

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Must Match	options	No	anyCondition	Whether rows must match any condition (OR) or all conditions (AND).
				Options: anyCondition, allConditions
Filter Conditions	json	No	[]	Array of filter conditions. Each condition: { “keyName”: “columnName”, “condition”: “eq|neq|gt|gte|lt|lte|like|ilike|isEmpty|isNotEmpty|isTrue|isFalse”, “keyValue”: “value” }. The keyValue field is not needed for isEmpty, isNotEmpty, isTrue, and isFalse conditions.
Return All	boolean	No	false	Whether to return all results or only up to a given limit.
Limit	number	No	50	Max number of results to return. (shown when Return All is false)
Order By	boolean	No	false	Whether to sort the results by a column.
Order By Column	string	No	createdAt	The column name to sort by. Must match an existing column in the data table. (shown when Order By is true)
Order By Direction	options	No	DESC	Sort direction for the column. (shown when Order By is true)
				Options: ASC, DESC

Row: Update

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Must Match	options	No	anyCondition	Whether rows must match any condition (OR) or all conditions (AND).
				Options: anyCondition, allConditions
Filter Conditions	json	No	[]	Array of filter conditions selecting the rows to update. At least one condition is required.
Column Mapping Mode	options	No	defineBelow	Whether to map input item fields automatically or define column values explicitly. Auto-map copies input JSON and strips system columns (id, createdAt, updatedAt).
				Options: autoMapInputData, defineBelow
Column Values	json	No	{}	JSON object with column names as keys and values to set. Example: {“name”: “John”, “age”: 30, “active”: true}. (shown when Column Mapping Mode is defineBelow)
Dry Run	boolean	No	false	Whether to simulate the operation and return affected rows without committing changes.

Row: Upsert

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Must Match	options	No	anyCondition	Whether rows must match any condition (OR) or all conditions (AND).
				Options: anyCondition, allConditions
Filter Conditions	json	No	[]	Array of filter conditions deciding whether a matching row already exists. At least one condition is required.
Column Mapping Mode	options	No	defineBelow	Whether to map input item fields automatically or define column values explicitly. Auto-map copies input JSON and strips system columns (id, createdAt, updatedAt).
				Options: autoMapInputData, defineBelow
Column Values	json	No	{}	JSON object with column names as keys and values to set. Example: {“name”: “John”, “age”: 30, “active”: true}. (shown when Column Mapping Mode is defineBelow)
Dry Run	boolean	No	false	Whether to simulate the operation and return affected rows without committing changes.

Row: Delete

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Must Match	options	No	anyCondition	Whether rows must match any condition (OR) or all conditions (AND).
				Options: anyCondition, allConditions
Filter Conditions	json	No	[]	Array of filter conditions selecting the rows to delete. At least one condition is required.
Dry Run	boolean	No	false	Whether to simulate the operation and return affected rows without committing changes.

Row: If Row Exists

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Must Match	options	No	anyCondition	Whether rows must match any condition (OR) or all conditions (AND).
				Options: anyCondition, allConditions
Filter Conditions	json	No	[]	Array of filter conditions the item is tested against.

Row: If Row Does Not Exist

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table. You can find table IDs in the data tables management page.
Must Match	options	No	anyCondition	Whether rows must match any condition (OR) or all conditions (AND).
				Options: anyCondition, allConditions
Filter Conditions	json	No	[]	Array of filter conditions the item is tested against.

Table: Create

Parameter	Type	Required	Default	Description
Table Name	string	Yes	—	The name of the data table to create.
Columns	json	No	[]	Array of column definitions for the new table. Each column: {“name”: “columnName”, “type”: “string|number|boolean|date”}. System columns (id, createdAt, updatedAt) are added automatically.
Reuse Existing Tables	boolean	No	true	Whether to return an existing table with the same name instead of throwing an error.

Table: List

Parameter	Type	Required	Default	Description
Return All	boolean	No	true	Whether to return all tables or only up to a given limit.
Limit	number	No	50	Max number of tables to return. (shown when Return All is false)
Filter by Name	string	No	—	Filter data tables by name (case-insensitive).
Sort Field	options	No	name	Field to sort the table list by.
				Options: createdAt, name, updatedAt
Sort Direction	options	No	asc	Sort direction for the table list.
				Options: asc, desc

Table: Update

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table to operate on.
New Name	string	Yes	—	The new name for the data table.

Table: Delete

Parameter	Type	Required	Default	Description
Data Table	string	Yes	—	The ID or name of the data table to operate on.

All Operations

Parameter	Type	Required	Default	Description
Max Concurrency	number	No	10	Maximum number of items to process concurrently.

Output Data

Except for the two conditional operations, this node replaces the item JSON with the operation’s result and does not forward binary data. Read anything you still need from the item before this node, or send it down a parallel branch.

Resource and operation	Items out and what is on them
Row: insert	One item holding the inserted row, including the system columns id, createdAt and updatedAt. With Optimize Bulk on, a count result is returned instead of the row data.
Row: get	One item per matching row. A query that matches nothing produces no output items at all.
Row: update	One item per updated row.
Row: upsert	One item per row that was updated or inserted.
Row: deleteRows	One item per deleted row.
Row: rowExists	The original input item, unchanged, when at least one row matches. When nothing matches the item is dropped and produces no output.
Row: rowNotExists	The original input item, unchanged, when no row matches. When something matches the item is dropped.
Table: create	One item holding the data table record, including its id and name. With Reuse Existing Tables on and a name that already exists, the existing table’s record is returned instead.
Table: list	One item per data table.
Table: update	One item: { "success": true, "name": "<the new name>" }.
Table: delete	One item: { "success": true, "deletedTableId": "<the table id>" }.

A row comes back with your own columns plus the three system columns:

{
  "id": 17,
  "name": "John Doe",
  "email": "john@example.com",
  "age": 30,
  "active": true,
  "createdAt": "2026-01-15T10:00:00.000Z",
  "updatedAt": "2026-01-15T10:00:00.000Z"
}

Date values always come back as ISO 8601 strings, so they are safe to compare and to pass to other nodes.

Dry Run changes nothing in storage but still emits the rows the operation would have touched, which makes it a safe way to preview a destructive filter before running it for real.

deleteRows, update and upsert require at least one filter condition — an empty Filter Conditions array fails the item rather than matching every row.

Because rowExists and rowNotExists drop non-matching items, they act as filters in the middle of a branch rather than as data sources.

Usage Examples

• Insert a row into a data table from workflow data
• Query rows from a data table with filters and sorting
• Delete rows matching specific conditions
• Update rows that match a filter
• Upsert: update if exists, insert if not
• Check if a row exists before proceeding
• Create a new data table with typed columns
• List all available data tables
• Rename or delete a data table

Example Configuration

Insert a row straight from the incoming item’s fields:

{
  "type": "data_table",
  "parameters": {
    "resource": "row",
    "operation": "insert",
    "dataTableId": "users_table",
    "columnsMappingMode": "autoMapInputData"
  }
}

Insert a row with values you set explicitly:

{
  "type": "data_table",
  "parameters": {
    "resource": "row",
    "operation": "insert",
    "dataTableId": "users_table",
    "columnsMappingMode": "defineBelow",
    "columnsValue": {
      "name": "John Doe",
      "email": "john@example.com",
      "age": 30,
      "active": true
    }
  }
}

Query rows with two conditions, a limit and a sort:

{
  "type": "data_table",
  "parameters": {
    "resource": "row",
    "operation": "get",
    "dataTableId": "users_table",
    "matchType": "allConditions",
    "filterConditions": [
      {
        "keyName": "active",
        "condition": "isTrue"
      },
      {
        "keyName": "age",
        "condition": "gte",
        "keyValue": "18"
      }
    ],
    "returnAll": false,
    "limit": 50,
    "orderBy": true,
    "orderByColumn": "name",
    "orderByDirection": "ASC"
  }
}

Preview an update without committing it:

{
  "type": "data_table",
  "parameters": {
    "resource": "row",
    "operation": "update",
    "dataTableId": "users_table",
    "matchType": "anyCondition",
    "filterConditions": [
      {
        "keyName": "email",
        "condition": "like",
        "keyValue": "@example.com"
      }
    ],
    "columnsMappingMode": "defineBelow",
    "columnsValue": {
      "status": "verified"
    },
    "dryRun": true
  }
}

Delete inactive rows that have never logged in:

{
  "type": "data_table",
  "parameters": {
    "resource": "row",
    "operation": "deleteRows",
    "dataTableId": "users_table",
    "matchType": "allConditions",
    "filterConditions": [
      {
        "keyName": "active",
        "condition": "isFalse"
      },
      {
        "keyName": "last_login",
        "condition": "isEmpty"
      }
    ]
  }
}

Let only items that already exist in the table continue:

{
  "type": "data_table",
  "parameters": {
    "resource": "row",
    "operation": "rowExists",
    "dataTableId": "users_table",
    "matchType": "allConditions",
    "filterConditions": [
      {
        "keyName": "email",
        "condition": "eq",
        "keyValue": "{{ $json.email }}"
      }
    ]
  }
}

Create a typed table, reusing it if it already exists:

{
  "type": "data_table",
  "parameters": {
    "resource": "table",
    "operation": "create",
    "tableName": "products",
    "tableColumns": [
      {
        "name": "name",
        "type": "string"
      },
      {
        "name": "price",
        "type": "number"
      },
      {
        "name": "in_stock",
        "type": "boolean"
      },
      {
        "name": "launch_date",
        "type": "date"
      }
    ],
    "createIfNotExists": true
  }
}

List tables whose name contains “user”:

{
  "type": "data_table",
  "parameters": {
    "resource": "table",
    "operation": "list",
    "returnAll": false,
    "limit": 20,
    "filterName": "user",
    "sortField": "name",
    "sortDirection": "asc"
  }
}

Rename a table:

{
  "type": "data_table",
  "parameters": {
    "resource": "table",
    "operation": "update",
    "dataTableId": "old_table_name",
    "newName": "new_table_name"
  }
}

Delete a table:

{
  "type": "data_table",
  "parameters": {
    "resource": "table",
    "operation": "delete",
    "dataTableId": "table_to_delete"
  }
}

Error Handling

Mode	Behavior
stop	Halts workflow on first error
continue	Skips failed items, passes successful ones through
errorPort	Routes failed items to Error output port

Tips

Persistent tabular storage for saving, querying, updating, and deleting structured data across workflow executions without external databases.

Common Patterns

• CRUD workflow — create the table (table / create), insert data (row / insert), then query it (row / get).
• Conditional processing — use rowExists or rowNotExists to branch workflow logic based on data presence.
• Bulk operations — turn Optimize Bulk on when inserting many rows, and Dry Run on when testing an update or delete filter.
• Advanced filtering — combine several conditions and set Must Match to allConditions for AND logic or anyCondition for OR logic.

Parameter Dependencies

• Resource determines which Operation options are available.
• Most row operations need a Data Table and support filtering via Must Match and Filter Conditions.
• Insert, update and upsert use Column Mapping Mode to decide how data reaches the columns.
• Get supports pagination (Return All, Limit) and sorting (Order By, Order By Column, Order By Direction).
• Table operations have their own parameters — Table Name when creating, New Name when renaming.

Filter conditions

• Comparison: eq, neq, gt, gte, lt, lte.
• Text: like and ilike (case-insensitive).
• Presence and boolean: isEmpty, isNotEmpty, isTrue, isFalse — these take no keyValue.