Batch processing is the practice of running a set of operations on a large group of records all at once, on a schedule or trigger, rather than handling each one individually as it comes in. In feed management, batch processing is what lets a system update, import, or export tens of thousands of product records in a single scheduled run — recalculating prices, refreshing stock levels, or regenerating an entire feed file — instead of processing each product change the moment it happens.

Why It Matters for Feed Management

Not every update needs to happen instantly, and trying to process every single change in real time across a catalog of hundreds of thousands of SKUs would strain both the feed platform and the channels receiving the data. Batch processing groups changes together and runs them during a defined window — nightly, hourly, or on demand — which is far more efficient for large-scale operations like re-exporting a full catalog to Google Merchant Center or recalculating margin-based pricing across every product in a category. It also makes large operations predictable and easier to monitor: a batch job either completes successfully or fails as a whole, which makes it simpler to catch and roll back a bad update than trying to track thousands of individual real-time changes. For anything where a short delay is acceptable, batch processing is generally cheaper and more stable than an always-on, event-by-event approach.

How It Works

A batch job typically pulls a full or partial data set — every product in a category, every item that changed since the last run, or the entire catalog — applies a set of transformations or rules to all of them at once, and then writes the result out as an updated feed file or a set of API calls. This is the mechanism behind most bulk editing features in feed platforms, where a rule like "increase all prices in the outdoor furniture category by 8%" gets applied across every matching product in a single pass rather than one at a time. Batch jobs are usually triggered by a schedule (every night at 2 AM) or an external signal (a new export request), and they form one of the core mechanisms behind feed automation, since a fully automated feed pipeline needs some way to reprocess the whole catalog periodically without manual intervention.

Example

<!-- Feed export metadata showing the result of a scheduled batch run -->
<feed>
  <batch_id>batch-2026-08-15-0200</batch_id>
  <items_processed>48213</items_processed>
  <run_type>scheduled</run_type>
  <item>
    <g:id>SKU-30099</g:id>
    <title>Outdoor Teak Dining Chair</title>
    <g:price>142.00 USD</g:price>
    <g:availability>in stock</g:availability>
  </item>
  <!-- ...remaining items in this batch... -->
</feed>

The batch_id and items_processed fields here reflect how a feed platform logs a single overnight run that touched every item in the catalog, rather than each product being updated as a separate real-time event.

Related Concepts

Batch processing is the engine underneath most large-scale bulk editing operations, since applying a rule to thousands of products at once only works efficiently if it's run as a single batch rather than one item at a time. It's also a foundational piece of broader workflow automation and feed automation pipelines, where scheduled batch runs are what keep an entire catalog synchronized across channels without anyone manually triggering each update.