This article provides a complete, practical walkthrough of the Manual Stickynote Automate Triggered n8n agent. It connects Merge 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 Merge, 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

• Merge

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:  
   Mastering Data Aggregation in n8n: Merging Ingredients, Recipes, and Rock Bands
   
   Meta Description:  
   Learn how to use n8n’s Merge node to replicate SQL-like joins for merging complex datasets, such as recipes and band members. This hands-on guide explores Combine, Enrich, and Union modes through a real-world workflow.
   
   Keywords:  
   n8n, Merge node, data aggregation, low-code automation, workflow automation, SQL joins, Left join, Inner join, Union All, recipe management, ingredients in stock, rock bands, Queen, Led Zeppelin
   
   Third-party APIs Used:  
   None – this n8n workflow is self-contained and relies solely on built-in nodes and JavaScript code nodes within n8n. No external integrations or third-party APIs are used.
   
   —
   
   Article:
   
   # Mastering Data Aggregation in n8n: Merging Ingredients, Recipes, and Rock Bands
   
   When it comes to automating workflows involving multiple data sources, having the right tools to combine, filter, and enrich data is essential. This is particularly true when mimicking SQL-like operations such as joins in a low-code environment. Enter the n8n Merge node — a powerful, flexible feature that allows users to execute various types of data combinations such as inner joins, left joins, and unions with ease.
   
   In this guide, we walk through an imaginative and educational n8n workflow that leverages the Merge node to perform multiple real-life inspired data operations — from managing recipes and checking pantry stock to forming a dream rock band. Whether you're an automation enthusiast, a developer, or simply someone looking to better organize your data using no-code/low-code principles, this walkthrough will show the magic behind n8n’s Merge node.
   
   ## Understanding the Workflow
   
   This workflow is kicked off manually using the Manual Trigger node and features several JavaScript code nodes that simulate data sources. These sources are then joined together using one of n8n’s most versatile nodes — the Merge node.
   
   The use cases implemented in this workflow reflect common data-handling patterns you may encounter in app development or administrative tasks.
   
   Let’s break this down into the three main scenarios presented in the workflow.
   
   ### 1. Filtering Ingredients — “Inner Join” Style
   
   In one scenario, we take two sets of data:
   
   - A. Ingredients Needed: a list of ingredients required for a recipe (Flour, Eggs, Milk, Lemon, Sugar)
   - B. Ingredients in Stock: a subset showing what's actually available (Eggs, Lemon, Sugar)
   
   Using the Merge node in “Combine” mode with matching fields (here, the “Name” property), we can simulate an SQL Inner Join. This results in a filtered list showing only the ingredients from list A that are also present in list B — effectively identifying what we can cook now based on current inventory.
   
   📌 Use case: Quickly determine which recipes you can prepare with available ingredients.
   
   ### 2. Enriching Recipe Data — “Left Join” Style
   
   In a second use case, we have:
   
   - A. Ingredients: the basic list of ingredients
   - B. Recipe Quantities: a list mapping some of those ingredients to required quantities
   
   Using the Merge node again in “Enrich Input 1” mode (which corresponds conceptually to a SQL Left Join), we append additional information to list A where a match is found in list B. For example, “Eggs” may be enriched with a quantity of 2, while ingredients that aren’t matched (like “Milk”) are still retained in the output but without a quantity assigned.
   
   📌 Use case: Build a full shopping list with what you already have and exactly what quantities you need.
   
   ### 3. Combining Band Members — “Union All” Style
   
   In a more whimsical example, the workflow mimics merging fan-favorite rock bands: Queen and Led Zeppelin.
   
   Two code nodes labeled:
   
   - A. Queen
   - B. Led Zeppelin
   
   contain arrays of member data (name, instrument, sometimes superpowers!) from both legendary bands. The Merge node is used here in default mode (without field keys), effectively combining both lists into one — akin to an SQL UNION ALL operation.
   
   Unlike SQL’s UNION, n8n’s Merge doesn’t require exact field matching. Fields unique to one dataset (e.g. “Superpower” or “Second Instrument”) are preserved and included wherever applicable, making this method more flexible than a traditional relational database union.
   
   📌 Use case: Aggregate data from disparate sources where field structures may not align perfectly.
   
   ## Merge Node Modes Recap
   
   Here’s a quick overview of the different Join strategies used in the workflow:
   
   | Merge Mode         | Data Behavior                                  | SQL Analogy     |
   |--------------------|------------------------------------------------|------------------|
   | Combine            | Keep only matches in both datasets             | INNER JOIN       |
   | Enrich Input 1     | Keep all from A, add fields from B if matched  | LEFT JOIN        |
   | Default (add items)| Append all items from both lists               | UNION ALL        |
   
   Each mode is designed for a particular data-handling purpose, making Merge one of the most useful core nodes in n8n.
   
   ## Advantages of Using Merge in n8n
   
   - 🧩 Modular: Use multiple Merge nodes to perform layered operations.
   - 🛠️ Low Code, High Functionality: Perfect for those not fluent in SQL or JavaScript.
   - 💡 Flexible: Works with mismatched fields, nested data, and various node types.
   - 🧬 Reproducible: Excellent for building data transformation pipelines.
   
   ## Final Thoughts
   
   This workflow serves as both a practical and imaginative sandbox for learning how to combine datasets using the Merge node in n8n. Whether you're checking ingredients for your dinner plans or forming the ultimate rock supergroup, the underlying principles of data merging remain the same.
   
   There are no third-party APIs required in this workflow — all operations are achieved using n8n's internal Code and Merge nodes, making it perfect for users experimenting locally or in learning environments.
   
   If you’re new to n8n or just exploring how it could streamline your data processes, this is a great place to start. Dive in, remix the data, and discover how powerful automation can be when you apply clever logic with just a few visual nodes.
   
   —
   
   Ready to build your own data mashups? Check out the Merge Node Documentation at [n8n.io](https://docs.n8n.io/integrations/core-nodes/n8n-nodes-base.merge/) and elevate your automation game today.

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.