> For the complete documentation index, see [llms.txt](https://docs.artinals.com/artinals-protocol/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.artinals.com/artinals-protocol/advanced-topics/batch-update-operations.md).

# Batch Update Operations

**Purpose**:\
Batch operations streamline repetitive tasks, such as updating hundreds of NFTs’ metadata or transferring large volumes of tokens at once. This approach:

* Reduces transaction overhead.
* Minimizes gas costs and improves user experience.
* Ensures atomicity—either all updates succeed, or none do, maintaining data consistency.

**Examples**:

* **Batch Minting (ART20)**: Minting large sets of NFTs in a single transaction.
* **Batch Metadata Update**: Quickly change the logo URI, description, or other attributes of multiple NFTs.
* **Batch Transfer or Burn**: Efficiently redistribute or burn large quantities of NFTs without multiple transactions.

**Best Practices**:

* Use batch operations for large-scale collections or events (e.g., seasonal updates).
* Combine batch metadata updates with value sources for dynamic, time-sensitive changes.
* Test these operations extensively before running them on live collections.