Introduction to Data Types
Data types define what kind of value a workflow handles like text, numbers, dates, booleans, lists, objects, or files. Knowing them ensures nodes read, compare, and transform data correctly instead of producing silent errors or misrouting logic.
n8n passes data between nodes as JSON in an array-of-items shape. If the type is wrong (string vs number, array vs object), nodes won't behave the way you expect.
When working with data in n8n, make sure each value is in the right shape before passing it downstream. Clean, normalized, and correctly typed data keeps workflows predictable and prevents silent failures.
- Coerce data early: convert strings to numbers before doing math;
- Normalize text: lowercase and trim before deduping or routing;
- Dates: generate with
{{$now}}, compare in IF (Date & Time), reformat with Date & Time node; - Arrays: use real arrays; Split Out to process items and Aggregate/Item Lists to recombine;
- LLM outputs: request strict JSON, then validate and normalize before merging.
With all of this, you should confidently recognize and convert data types, handle dates and arrays properly, manage binary files when needed, and quickly resolve type mismatches that disrupt workflow logic.
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Can you explain how to convert between these data types in n8n?
What are some common errors caused by type mismatches in n8n?
How do I validate and normalize data before passing it to the next node?
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Data types define what kind of value a workflow handles like text, numbers, dates, booleans, lists, objects, or files. Knowing them ensures nodes read, compare, and transform data correctly instead of producing silent errors or misrouting logic.
n8n passes data between nodes as JSON in an array-of-items shape. If the type is wrong (string vs number, array vs object), nodes won't behave the way you expect.
When working with data in n8n, make sure each value is in the right shape before passing it downstream. Clean, normalized, and correctly typed data keeps workflows predictable and prevents silent failures.
- Coerce data early: convert strings to numbers before doing math;
- Normalize text: lowercase and trim before deduping or routing;
- Dates: generate with
{{$now}}, compare in IF (Date & Time), reformat with Date & Time node; - Arrays: use real arrays; Split Out to process items and Aggregate/Item Lists to recombine;
- LLM outputs: request strict JSON, then validate and normalize before merging.
With all of this, you should confidently recognize and convert data types, handle dates and arrays properly, manage binary files when needed, and quickly resolve type mismatches that disrupt workflow logic.
Tack för dina kommentarer!
