$5/1000
results
Fetch detailed YouTube video metadata — titles, stats, descriptions, and channel context — from URLs or video IDs. $5.00 per 1,000 videos.
Fetch detailed YouTube video metadata — titles, stats, descriptions, and channel context — from URLs or video IDs. $5.00 per 1,000 videos.
Hydrate video IDs/URLs into full metadata rows for catalogs and enrichment.
On Scrapy.io, Scraping Dino YouTube Video Metadata by Scraping Dino is built for scraping youtube workflows — run from the Store, schedule recurring jobs, or POST to scrapingdino.p.scrapy.io from your stack.
YouTube is the largest public video catalog — channel stats, upload cadence, comments, and recommendation graphs power creator research, competitive intelligence, and audience insight. A scraping youtube job on Scrapy.io hydrates channel URLs, video IDs, or search queries into structured JSON without maintaining your own headless browser farm.
Scraping Dino YouTube Video Metadata supports batch inputs (channels, video IDs, or keywords) with per-target caps so nightly jobs stay within budget. Export to JSON, CSV, or Excel, or POST the same payload to scrapingdino.p.scrapy.io from your backend.
Scraping Dino YouTube tools chain naturally: resolve channels, list uploads or Shorts, scrape comments on top performers, or expand related videos for graph analysis — all with warehouse-friendly schemas.
This scraping youtube on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover Scraping Dino YouTube Video Metadata while researching scraping youtube and related terms such as youtube scraper, scrape youtube, youtube scrape, youtube scraping. This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of Scraping Dino YouTube Video Metadata:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetscrapingdino.p.scrapy.io/youtube-video-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Scraping Dino YouTube Video Metadata so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Hydrate metadata for specific video URLs or IDs.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"videoIds": [
"https://www.youtube.com/watch?v=dQw4w9WgXcQ"
]
}| Field | Type | Required | Description |
|---|---|---|---|
videoIds | string[] | Yes | YouTube video URLs or 11-character video IDs |
Results appear in the Scrapy.io run Output tab. Video metadata rows and Errors.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
[
{
"type": "video",
"videoId": "dQw4w9WgXcQ",
"title": "Example Video",
"description": "Full video description text.",
"viewCount": 1200000000,
"channelId": "UCxxxxxxxx",
"publishedTime": "2009-10-25T00:00:00.000Z"
}
]Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
fill missing titles/stats in internal libraries
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
store view/like baselines over time
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
archive metadata for referenced videos
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
compare formats and lengths in a niche
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
URL-in / metadata-out with warehouse-grade consistency. Prefer Scraping Dino when video hydration is a scheduled enrichment job next to your channel and comment pipelines.
Scraping Dino YouTube Video Metadata is rarely the only step in a data workflow. Teams typically:
videoId across your YouTube warehouse tablesSee Related Scraping Dino tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersScraping Dino YouTube Video Metadata separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Scraping Dino tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Reddit, Maps, or YouTube extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Scraping Dino YouTube Video Metadata on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Scraping Dino listings add warehouse-first documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
Scraping Dino YouTube Video Metadata runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run Scraping Dino YouTube Video Metadata once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
scrapingdino.p.scrapy.io on a queueScraping Dino tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, maxReviews, or equivalent limits in the input form.
curl -X POST "https://scrapingdino.p.scrapy.io/youtube-video-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"videoIds": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://scrapingdino.p.scrapy.io/youtube-video-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"videoIds": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Scraping Dino YouTube Video Metadata is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on Scraping Dino YouTube Video Metadata for MCP connection snippets.
| Tool | Best for |
|---|---|
| Scraping Dino YouTube Channel Profiles | Scraping Dino companion tool — overview |
| Scraping Dino YouTube Channel Shorts | Scraping Dino companion tool — overview |
| Scraping Dino YouTube Channel Videos | Scraping Dino companion tool — overview |
Batch thousands of video IDs per run for catalog hydration
Schedule refreshes on high-priority URLs weekly
Inspect per-row errors in the Errors output view instead of failing an entire batch
When Maps, Reddit, or YouTube batches repeat the same error, raise Issues on this Scraping Dino listing with your run ID and a sample input
Scraping Dino YouTube Video Metadata collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$5.00 per 1,000 videos. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://scrapingdino.p.scrapy.io/youtube-video-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query Scraping Dino YouTube Video Metadata from AI assistants and agent workflows.
Scraping Dino maintains deep Maps, Reddit, and YouTube coverage — star the tool in Reviews if exports are landing in your warehouse and mention which dataset you rely on most.
Hydrate video IDs/URLs into full metadata rows for catalogs and enrichment.
On Scrapy.io, Scraping Dino YouTube Video Metadata by Scraping Dino is built for scraping youtube workflows — run from the Store, schedule recurring jobs, or POST to scrapingdino.p.scrapy.io from your stack.
YouTube is the largest public video catalog — channel stats, upload cadence, comments, and recommendation graphs power creator research, competitive intelligence, and audience insight. A scraping youtube job on Scrapy.io hydrates channel URLs, video IDs, or search queries into structured JSON without maintaining your own headless browser farm.
Scraping Dino YouTube Video Metadata supports batch inputs (channels, video IDs, or keywords) with per-target caps so nightly jobs stay within budget. Export to JSON, CSV, or Excel, or POST the same payload to scrapingdino.p.scrapy.io from your backend.
Scraping Dino YouTube tools chain naturally: resolve channels, list uploads or Shorts, scrape comments on top performers, or expand related videos for graph analysis — all with warehouse-friendly schemas.
This scraping youtube on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover Scraping Dino YouTube Video Metadata while researching scraping youtube and related terms such as youtube scraper, scrape youtube, youtube scrape, youtube scraping. This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of Scraping Dino YouTube Video Metadata:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetscrapingdino.p.scrapy.io/youtube-video-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Scraping Dino YouTube Video Metadata so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Hydrate metadata for specific video URLs or IDs.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"videoIds": [
"https://www.youtube.com/watch?v=dQw4w9WgXcQ"
]
}| Field | Type | Required | Description |
|---|---|---|---|
videoIds | string[] | Yes | YouTube video URLs or 11-character video IDs |
Results appear in the Scrapy.io run Output tab. Video metadata rows and Errors.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
[
{
"type": "video",
"videoId": "dQw4w9WgXcQ",
"title": "Example Video",
"description": "Full video description text.",
"viewCount": 1200000000,
"channelId": "UCxxxxxxxx",
"publishedTime": "2009-10-25T00:00:00.000Z"
}
]Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
fill missing titles/stats in internal libraries
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
store view/like baselines over time
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
archive metadata for referenced videos
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
compare formats and lengths in a niche
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Scraping Dino with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
URL-in / metadata-out with warehouse-grade consistency. Prefer Scraping Dino when video hydration is a scheduled enrichment job next to your channel and comment pipelines.
Scraping Dino YouTube Video Metadata is rarely the only step in a data workflow. Teams typically:
videoId across your YouTube warehouse tablesSee Related Scraping Dino tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersScraping Dino YouTube Video Metadata separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Scraping Dino tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Reddit, Maps, or YouTube extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Scraping Dino YouTube Video Metadata on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Scraping Dino listings add warehouse-first documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
Scraping Dino YouTube Video Metadata runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run Scraping Dino YouTube Video Metadata once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
scrapingdino.p.scrapy.io on a queueScraping Dino tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, maxReviews, or equivalent limits in the input form.
curl -X POST "https://scrapingdino.p.scrapy.io/youtube-video-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"videoIds": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://scrapingdino.p.scrapy.io/youtube-video-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"videoIds": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Scraping Dino YouTube Video Metadata is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on Scraping Dino YouTube Video Metadata for MCP connection snippets.
| Tool | Best for |
|---|---|
| Scraping Dino YouTube Channel Profiles | Scraping Dino companion tool — overview |
| Scraping Dino YouTube Channel Shorts | Scraping Dino companion tool — overview |
| Scraping Dino YouTube Channel Videos | Scraping Dino companion tool — overview |
Batch thousands of video IDs per run for catalog hydration
Schedule refreshes on high-priority URLs weekly
Inspect per-row errors in the Errors output view instead of failing an entire batch
When Maps, Reddit, or YouTube batches repeat the same error, raise Issues on this Scraping Dino listing with your run ID and a sample input
Scraping Dino YouTube Video Metadata collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$5.00 per 1,000 videos. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://scrapingdino.p.scrapy.io/youtube-video-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query Scraping Dino YouTube Video Metadata from AI assistants and agent workflows.
Scraping Dino maintains deep Maps, Reddit, and YouTube coverage — star the tool in Reviews if exports are landing in your warehouse and mention which dataset you rely on most.
