Liam, a senior developer advocate and n8n super fan, announces the release of native data tables in n8n. He walks through what’s new, how they work, and why they matter for workflows.
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What’s new / Key announcement
- 🚀 A new Data Tables tab is added next to Workflows and Credentials.
- 🗂️ Project-scoped: tables are accessible only within their own project’s workflows.
- 🧭 Availability: self-hosted users get tables too, but not projects on the community plan.
- 🧱 Built-in database: data tables are powered by n8n’s built-in database, with no external credentials required.
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How it works
- 🧩 Data types: you must set a type for each column (e.g., string); if unsure, default to string.
- ➕ Editing/adding rows: you can manually add rows on the data table page.
- 🔗 Node integration: use the data tables node like a regular node—connect to a table to read/write data.
- 🔒 Security and speed: no third-party credentials needed; data stays in the built-in database, often faster than external DBs.
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Access scope and availability
- 🗺️ Project scope: tables are scoped to the project; accessible from workflows within that project.
- 🖥️ Self-hosted users: supported; but note the community plan limitation regarding projects.
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Benefits and architecture
- ⚡ Built-in database: fast to get started and can be faster than external databases depending on use case.
- 🛡️ No external credentials: simplifies setup and reduces security concerns.
- 🧭 Local data handling: avoids round trips to external services, improving responsiveness.
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Performance comparison
- ⏱️ Data Tables: inserting one row takes 8 ms.
- 📈 Google Sheets: inserting the same row takes 1020 ms (about 1.02 seconds).
- 🥇 Takeaway: data tables are ~100x faster in this scenario, thanks to in-process storage and no internet round-trips.
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Use cases and audience
- 🤖 AI agent integration: data tables support all the same node functionality, making them ideal for AI-enabled workflows.
- 🎯 Ideal for fast, in-workflow data storage and rapid iteration.
- 💡 Future use cases: Liam asks for audience ideas in the comments—potential areas include rapid prototyping, agent-powered automation, and more.
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Next steps / call to action
- 🚀 Upgrade to the new version to access data tables.
- 💬 Share use cases and feedback in the comments; more videos on use cases are planned.
- 🎬 Stay tuned for upcoming videos and experiments with data tables.
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Takeaway
- Native data tables in n8n deliver fast, secure, built-in data storage with seamless node integration, opening new possibilities for AI-enabled and high-speed workflows.