1. Working with complex data flows
N8n teaches advanced workflow building by explaining node execution, data branching, and merging techniques.
Mastering Workflow Building: Understanding Node Execution and Data Management
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📹 Welcome to the first video in the n8n Advanced Course, where you'll become a professional workflow builder by looking at how data flows through an n8n workflow .
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🔄 This session focuses on the node execution order of workflows created in version 1.0 and later, emphasizing that branches are executed sequentially from top to bottom, left to right .
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🔍 For example, in a given workflow, the node execution order is: weight , weight 1 , weight 2 , weight 3 , weight 4 , weight 5 .
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📊 To efficiently manage branches that depend on each other's output, it is better to merge them before using the data, rather than changing the execution order .
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🔀 If nodes and switch nodes are essential for splitting data into multiple branches , allowing you to perform separate actions based on conditions, while switch nodes allow you to create** multiple branches from a single input.
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🔗 The merge node also plays an important role in **combining data from different branches and effectively manages the output by providing options such as append, join, or select .
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⚙ Finally, the Loop Over Items node is useful for handling large data sets and API rate limits by batching items to ensure efficient execution without system overload .
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📅 Thanks for joining us for this session, stay tuned for the next video where we'll dive deeper into the more advanced n8n node .
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This video explains how data flows within a workflow and the order in which branches are executed.
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N8n executes branches sequentially starting from version 1.0.
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Branches are executed from top to bottom, left to right across the screen.
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If there are dependencies between branches, it is better to merge the branches rather than change the order in which they are executed.
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If node : Can create branches depending on whether a condition is true or false.
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Switch node : You can split data by creating multiple branches.
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You can use more than one output branch to send the same data to each branch.
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Merge node : Allows you to merge data from multiple branches.
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Merge method: Append, Combine, Choose.
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Similar to the concept of SQL joins (Inner Join, Left Join, etc.).
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Loop Over Items node : Useful when processing a lot of data or specific nodes.
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Data can be processed in multiple batches to avoid rate limits that may occur in API calls.
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Explains how to check the execution status of the Loop Over Items node .
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You can track and verify data from each iteration.
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Example of branch execution: Executed sequentially from upper branch to lower branch.
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When building a workflow, it is necessary to consider the order of branch execution.
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08:45 – 10:18:
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Merge node example: Branch personal and work emails and then merge them.
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Merge two branches using the Append option, merging them into a total of 10 items.
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10:18 – 12:49:
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Data Enrichment Example: Read company information from Google Sheets and merge contact data based on domain.
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Extract email domains and match them with your company domain.
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12:49 – 14:23:
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Add company information related to your contacts using merged data.
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Extend your data by adding company website and country information to each contact.
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14:23 – 15:10:
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When merging data through API calls, merging is possible based on position rather than field.
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15:10 – 16:09:
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Execute the entire workflow: split the two branches, merge the data, and produce the results.
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Shows how to extend data through data branching and merging.
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16:09 – End:
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This video covers node execution order, branch creation, and merging.
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In the next video we'll go into more advanced nodes in depth.