Apify Dataset

Apify is a web scraping and automation platform that lets you extract data from websites using pre-built or custom actors (scrapers). When an actor runs, it stores its output in a dataset — a structured collection of items you can export and analyze. Connecting Apify datasets to Coupler.io lets you pull that scraped data into your preferred destination automatically.

Why connect Apify Dataset to Coupler.io?

• Centralize scraped data — move web-extracted data into Google Sheets, BigQuery, Excel, or Looker Studio without manual exports

• Combine with other sources — use Append or Join transformations to merge datasets from multiple actors or runs

• Send to AI tools — pipe scraped content into ChatGPT, Claude, Gemini, or Perplexity for analysis, summarization, or classification

• Keep data fresh — schedule recurring syncs so your destination always reflects the latest actor output

Prerequisites

• An active Apify account with at least one completed actor run that has produced dataset output

• Your Apify API token (found in Apify Console under Settings > Integrations)

• The Dataset ID of the dataset you want to export (found in Storage > Datasets in Apify Console)

Quick start

If you want to export items from a website content crawler actor, choose the Item collection website content crawlers entity — it maps the crawler output fields automatically.

How to connect

1

Create a new data flow in Coupler.io. From your Coupler.io dashboard, click Add data flow and search for Apify Dataset as your source.

2

Enter your Apify API token. In the source settings, paste your API token from Apify Console (Settings > Integrations > API token). Coupler.io uses this to authenticate requests on your behalf.

3

Enter your Dataset ID. In the Dataset ID field, paste the ID of the dataset you want to export. You can find it in Apify Console under Storage > Datasets — click on the dataset and copy the ID from the URL or the detail panel.

4

Choose an entity. Select one of the available entities depending on what you want to export — see the table below for a quick guide.

5

Choose a destination. Pick where you want your data to land — Google Sheets, Excel, BigQuery, Looker Studio, or an AI destination like ChatGPT, Claude, Gemini, Cursor, Perplexity, or OpenClaw.

6

Run the data flow. Click Run to execute a manual sync and confirm data is flowing correctly before setting up a schedule.

Apify Dataset 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

• Load raw item collections into BigQuery for transformation and analysis at scale

• Join dataset metadata with item collections to enrich records with actor run context (run ID, creation date)

• Schedule syncs to keep a data warehouse table updated after each actor run

Developers

• Use Dataset collections to programmatically audit all datasets stored in an account

• Export item collections to Excel or Looker Studio for stakeholder-ready reporting without writing custom export scripts

• Pipe website content crawler output into Cursor or Claude for AI-assisted code or content generation workflows

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

Apify Dataset common issues

Connection issues

Authentication failed or API token not accepted

Double-check that you copied the full API token from Apify Console under Settings > Integrations. Tokens are long strings — make sure there are no trailing spaces or missing characters. If you recently regenerated your token, update it in the Coupler.io source settings.

Data flow connects but returns no data

Confirm that the Dataset ID you entered matches an existing dataset in Storage > Datasets in Apify Console. If the actor run failed or produced zero output, the dataset will exist but be empty. Run the actor again in Apify and verify it has items before re-running the data flow.

Missing data

Only some items are returned, not all

Apify datasets can be very large. Coupler.io paginates through all items automatically, but if you interrupted a run or the actor run itself was incomplete, the dataset may not contain all expected records. Check the itemCount field on the Datasets entity to confirm the expected number of items.

Expected fields are missing from item collection output

Item fields are defined by the actor, not by Apify or Coupler.io. If a field you expect isn't showing up, the actor may not have produced it in that run (e.g., a page returned no price data). Check the raw dataset in Apify Console to confirm whether the field exists in the source data.

Website content crawler entity returns fewer fields than expected

The Item collection website content crawlers entity uses a fixed set of fields designed for Apify's official website content crawler actors. If you're using a custom or third-party actor that produces different output, use the Item collections entity instead to get all raw fields.

Permission errors

403 Forbidden or "Dataset not found" error

The dataset you're trying to access may belong to a different Apify user or organization. Your API token only grants access to datasets owned by your account. If you're working in a shared Apify organization, make sure you're using a token with the correct organization scope.

Data discrepancies

Item count in Coupler.io doesn't match Apify Console

Apify reports both itemCount (all items) and cleanItemCount (deduplicated, non-empty items). Coupler.io exports all items by default. If your destination shows fewer rows than itemCount, check whether any items were empty objects — these may be filtered depending on your destination settings.

Data from a previous actor run is still showing up

Each actor run creates a new dataset with a new ID. If your data flow is pointing to the old Dataset ID, it will keep returning data from the old run. After each actor run, update the Dataset ID in your Coupler.io source settings to point to the new dataset.

Rate limits

Requests are being throttled or failing intermittently

Apify's API enforces rate limits that vary by plan. If you're running multiple data flows pointing to different datasets simultaneously, you may hit these limits. Stagger your data flow schedules or consolidate related datasets using the Append transformation to reduce the number of API calls.

Apify Dataset FAQ

Where do I find my Apify API token?

Go to your Apify Console and navigate to Settings > Integrations. Your API token is listed there. Copy the full token and paste it into the Coupler.io source settings when setting up your data flow.

Where do I find the Dataset ID?

In Apify Console, go to Storage > Datasets. Click on the dataset you want to export — the Dataset ID appears in the URL and in the dataset detail panel. It looks like a short alphanumeric string (e.g., RpXUj3dN9BsLmFv4).

Which entity should I use — Item collections or Item collection website content crawlers?

If your actor is Apify's official Website Content Crawler (or a compatible variant), use Item collection website content crawlers — it maps fields like url, title, text, and markdown automatically. For any other actor, use Item collections to get all raw fields as the actor produced them.

Can I export data from multiple Apify actor runs at once?

Yes. Each actor run produces a separate dataset with its own ID. You can create multiple sources within a single Coupler.io data flow — one per Dataset ID — and use the Append transformation to combine them into a single table. This is useful for tracking data across weekly scraping runs, for example.

Can I send Apify scraped content to an AI tool like ChatGPT or Claude?

Yes. Coupler.io supports AI destinations including ChatGPT, Claude, Gemini, Perplexity, Cursor, and OpenClaw. The Item collection website content crawlers entity is especially useful here because it provides a markdown field with clean, formatted page content that AI models handle well.

My actor ran again and the data hasn't updated — why?

Each Apify actor run creates a brand new dataset with a new ID. If your data flow is still pointed at the old Dataset ID, it will return data from the old run. You need to update the Dataset ID in your Coupler.io source settings to point to the new dataset after each run.

Can I use Coupler.io to monitor all datasets in my Apify account?

Yes. Use the Dataset collections entity, which returns a list of all datasets in your account along with metadata like itemCount, modifiedAt, and actRunId. This is a good way to build a dashboard that tracks which actors are running and how much data they're producing.

What happens if my Apify dataset is very large?

Coupler.io paginates through large datasets automatically — you don't need to configure anything special. For very large datasets (hundreds of thousands of rows), we recommend using BigQuery as your destination rather than Google Sheets, which has row limits and can slow down significantly with large payloads.

For a full breakdown of available fields and entities, see Data Overview. If you're running into sync problems, check Common Issues for specific fixes.