n8n Compare Datasets Node (Full Guide)

💼 Business owner or operator with a team? We build AI automation systems that cut costs and scale ops — done for you: https://ryanandmattdatascience.com/ai... 🚀 Want to make money with AI skills? Join our free community — real projects, real client strategies, and the exact stack we use: https://www.skool.com/data-and-ai Learn how to use the n8n Compare Datasets node to automatically find differences and similarities between two data sources. This tutorial walks you through comparing CSVs, JSON data, or database results in your n8n workflows. 🍿 WATCH NEXT N8N Playlist:    • n8n Full Course (Free Bootcamp) - Learn Ho...   In this comprehensive n8n tutorial, I break down the Compare Data Sets node and show you exactly how to spot differences between two data inputs. This underutilized node is perfect for comparing lead lists, identifying duplicates, and managing data from multiple sources. We explore all four output branches—differences in A, differences in B, same data, and different rows—so you can confidently handle any comparison scenario. I walk through five detailed examples using real restaurant lead data to demonstrate every setting and configuration option. You'll learn when to use "input A version" versus "input B version" as your source of truth, how to work with mixed versions for different fields, and what to do when multiple matches appear in your datasets. Each example builds on the last, from simple single-column comparisons to complex multi-field matching strategies. By the end of this video, you'll understand how to configure field matching, interpret the four different output branches, handle duplicate entries, and choose the right comparison method for your workflow. Whether you're managing CRM data, cleaning up contact lists, or merging datasets from different sources, this node will save you hours of manual comparison work. Perfect for anyone working with lead generation, data management, or automation workflows in n8n. TIMESTAMPS 00:00 Introduction to Compare Data Sets Node 01:07 Where to Find the Node & Basic Overview 02:17 Understanding the Data Sets 04:13 Exploring Different Output Branches 05:43 Example 1: Comparing by Company Field 08:15 Understanding Source of Truth (Input A vs B) 10:21 Example 2: Comparing Multiple Columns 13:04 Example 3: Using Mixed Versions 15:28 Example 4: Including Both Versions 17:33 Example 5: Handling Multiple Matches 19:44 Final Recap & Key Takeaways OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.