This article provides a complete, practical walkthrough of the Postgres Code Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating YouTube Video Analytics with n8n: Fetching, Filtering & Storing Performance Data
   
   Meta Description:  
   Discover how a powerful n8n workflow automates the process of fetching YouTube video statistics, filtering out shorts, and saving high-performing content in a PostgreSQL database. Perfect for content marketers and data analysts.
   
   Keywords:  
   n8n workflow, YouTube API, video analytics, PostgreSQL, YouTube automation, content marketing, video data analysis, Google API, performance metrics, no-code automation
   
   Third-Party APIs Used:
   
   1. Google YouTube Data API v3
   2. PostgreSQL database (via n8n connection)
   3. n8n nodes (HTTP, YouTube OAuth2, Postgres integration)
   
   —
   
   Article:
   
   Automating YouTube Video Performance Insights with n8n
   
   If you're managing multiple YouTube channels, tracking video performance manually can be a daunting task. From identifying viral content to evaluating overall engagement trends, the process can become inefficient without automation. This is where no-code/low-code tools like n8n come in. In this article, we’ll explore a sophisticated n8n workflow that automates the discovery, filtering, analysis, and storage of YouTube video performance metrics — all without writing a massive backend service.
   
   Overview: The Workflow in Action
   
   At a high level, this n8n workflow is designed to:
   
   1. Take a list of YouTube channels as input.
   2. Check the last recorded video upload time from a PostgreSQL database.
   3. Fetch new video uploads from the YouTube Data API.
   4. Filter out low-interest or short-form videos (YouTube Shorts).
   5. Retrieve key performance indicators — views, likes, comments.
   6. Insert the cleaned data into a PostgreSQL database.
   7. Generate insights on high-performing videos based on recent data.
   
   Let’s break each part down into detail:
   
   1. Channel Input & Loop Initiation
   
   The workflow begins with either a manual trigger or an Execute Workflow Trigger node. A list of YouTube channel IDs is hardcoded or passed as input, where each channel is looped over using the Loop Over Items node (SplitInBatches).
   
   2. Retrieving Recent Upload Info
   
   For each channel, the workflow queries the PostgreSQL database to find the most recently inserted video based on its publish time. This ensures that already-imported videos are skipped.
   
   3. Fetching New Video Data
   
   Using n8n's native YouTube node (authenticated with OAuth2), the workflow fetches up to 50 new videos filtered by publish date. If no data is returned (the channel hasn't uploaded new content), a message is logged and the iteration ends.
   
   4. Fetching Full Video Metrics from YouTube API
   
   The workflow invokes the Google YouTube Data API v3 directly via an HTTP Request node to fetch detailed statistics and content metadata (like view count, comments, video duration). This is essential for digging into the performance of each video.
   
   5. Filtering Shorts (Short-Form Videos)
   
   One clever node includes a JavaScript filter step that parses the ISO8601 duration string to remove videos under 3.5 minutes (210 seconds). This smartly eliminates typical Shorts content from affecting performance metrics.
   
   6. Mapping & Structuring Data for PostgreSQL
   
   The remaining data is mapped into a clean JSON format, ensuring null values for likes and comments are replaced with zeros. A dynamic SQL INSERT statement is also generated using another JavaScript code node to populate the "video_statistics" table properly.
   
   7. Writing to PostgreSQL
   
   With the prepared INSERT query and parameters, the workflow writes the full cleaned dataset into a PostgreSQL database. This database becomes a deep well for subsequent video performance analysis.
   
   8. Analyzing Channel Performance
   
   Another branch of the workflow involves an advanced SQL query that computes average view counts while excluding outliers (top 2 and bottom 2 performing videos per channel unless under 10 total). Then, a final Code node filters for videos posted within the last 14 days that have at least 2x average views, assigning them a simple like/view-based "score." These become your top-performing recent videos.
   
   Why This Matters
   
   This workflow is more than just a data pipeline — it's an intelligent automation engine for scalable YouTube analytics. Content marketers, social media managers, and data-driven creators can immediately benefit from:
   
   - Reliable content analysis without manual effort.
   - Performance benchmarking across channels.
   - A sustainable routine for triggering deeper assessments or alerts.
   
   Technical Strengths
   
   - Uses both n8n’s No-Code nodes and custom JavaScript logic where necessary.
   - Makes use of SQL CTEs (Common Table Expressions) in PostgreSQL to reduce outlier bias.
   - Fully modular — new metrics or filters can be added effortlessly.
   
   Conclusion
   
   By connecting YouTube’s expansive Data API with PostgreSQL through a structured and filter-centered workflow in n8n, this automation delivers powerful, repeatable, and scalable insights. Whether you're an individual creator or part of a media agency, automating your video analytics can give you back time while empowering smarter decisions.
   
   Try it, adapt it, evolve your automation stack — and let your performance data work for you.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.