Store Data Natively in n8n [Data Tables Demo]

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.