Data Overview

Apify datasets store the output of actor runs as structured collections of items — essentially rows of scraped or processed data. The exact fields in each item depend on the actor that produced them, but Coupler.io supports four entities that let you access both metadata and the raw item content.

Entities and what they return

Entity	Description
Dataset collections	Returns a list of all datasets in your Apify account, including IDs, names, and creation dates
Datasets	Returns metadata for a specific dataset: item count, size, creation date, and last modified time
Item collections	Returns the full list of items (rows) stored in a specific dataset — fields vary by actor
Item collection website content crawlers	Returns items from website content crawlers with standardized fields like URL, page title, and crawled text

Available fields

Dataset collections fields

Field	Description
id	Unique identifier for the dataset
name	Dataset name (if set)
createdAt	Timestamp when the dataset was created
modifiedAt	Timestamp of the last modification
accessedAt	Timestamp of the last access
itemCount	Number of items stored in the dataset
cleanItemCount	Number of items excluding empty or duplicate records

Dataset metadata fields

Field	Description
id	Dataset ID
name	Dataset name
userId	ID of the Apify user who owns the dataset
createdAt	Creation timestamp
modifiedAt	Last modified timestamp
itemCount	Total item count
cleanItemCount	Clean item count
actId	ID of the actor that created this dataset
actRunId	ID of the specific actor run that produced the data

Item collection fields

Fields vary depending on the actor that produced the dataset. Common fields include:

Field	Description
url	The URL that was scraped
title	Page or record title
description	Short description or meta description
text	Extracted text content
price	Price (e-commerce actors)
imageUrl	Image URL
Custom fields	Any additional fields output by the actor

Item collection website content crawler fields

Field	Description
url	Crawled page URL
title	Page title
text	Full extracted text from the page
markdown	Page content in Markdown format
metadata	Additional page metadata
crawl	Crawl metadata (depth, referrer URL, etc.)

Common field combinations

• Content audits: url + title + text from Item collection to review all scraped pages

• Dataset monitoring: itemCount + cleanItemCount + modifiedAt from Datasets to track actor run output over time

• AI analysis: url + markdown from Item collection website content crawlers, piped into ChatGPT or Claude for summarization

• Cross-dataset comparison: Use Append transformation to stack item collections from multiple actor runs into one unified table

Use cases by role

Marketers

• Pull competitor pricing data scraped by an Apify actor into Google Sheets for weekly tracking

• Send crawled website content to ChatGPT or Gemini for automated content gap analysis

• Append item collections from multiple scraping runs to build a historical dataset of SERP results

Data teams

Developers

Platform-specific notes

• Item fields are actor-dependent — the schema of Item collection data will differ between actors (e.g., an e-commerce scraper vs. a news scraper)

• The cleanItemCount may be lower than itemCount if the actor produced empty or duplicate records

• Apify datasets are tied to a specific actor run; if you re-run an actor, it creates a new dataset with a new ID

• For website content crawlers, the markdown field is the most useful for feeding content into AI destinations

• Very large datasets (millions of items) may require pagination — Coupler.io handles this automatically