You check your supplier import log and your stomach drops. Eighteen thousand products. “Skipped.” Every single one.

The instinct is to assume something has broken. A connection has failed. The sync isn’t working. You start troubleshooting a problem that, in most cases, doesn’t actually exist.

If your catalogue is large — thousands or tens of thousands of SKUs across one or more suppliers — you’ve probably seen this before, and you’ve probably had that same moment of panic. The good news is that “Skipped — no changes detected” is usually not a failure message. It’s confirmation that your import system is working exactly as designed, using a method called delta sync.

This post explains what delta sync updates actually do, why “Skipped” isn’t the problem it looks like, and why this matters for any retailer running a catalogue too large to re-import from scratch every time.

 

Why Full Re-Imports Become a Problem as Your Catalogue Grows

When you’re managing a small supplier catalogue — a few hundred products — re-importing everything on every sync isn’t a big deal. The system processes the data, updates what needs updating, and finishes quickly.

The problem appears as your catalogue scales. A retailer running 20,000 SKUs across multiple suppliers, syncing on a regular schedule, can’t realistically re-process every single product every time a sync runs. Doing so is slow — a full re-import of a large catalogue can take significantly longer than an update that only touches what’s actually changed. It’s also riskier: every full pass through the entire catalogue is an opportunity for something to go wrong, whether that’s a timeout, a partial failure, or unnecessary load on both your store and the supplier’s system.

Most of a supplier’s catalogue doesn’t change between syncs. Of 20,000 products, maybe 200 had a stock level update, a handful had a price change, and the rest are exactly as they were yesterday. Re-processing all 20,000 to update the 200 that actually changed is wasted effort — and at scale, that waste compounds into real time and reliability costs.

This is the problem delta sync exists to solve.

 

What Is Delta Sync?

Delta sync is an approach where, instead of re-processing an entire product catalogue on every import, the system identifies only the products that have actually changed since the last sync — and updates only those.

The word “delta” refers to the difference, or the change, between two states. A delta sync looks at what’s different between your supplier’s current data and what was there last time, and processes only that difference. Everything else is left alone because there’s nothing to update.

This is sometimes also called incremental sync, because it processes data incrementally — in the smaller increments that have actually changed — rather than the entire dataset every time.

 

How Delta Sync Actually Identifies What Changed

There are two common methods systems use to detect change, and understanding both helps explain why “Skipped” is the expected outcome for most products in most syncs.

Timestamp comparison

Many supplier feeds include a “last updated” timestamp on each product record. When a sync runs, the system compares that timestamp to the last time it processed that product. If the timestamp hasn’t moved, nothing has changed on the supplier’s side, and the product is skipped.

Timestamp comparison is fast and simple, but it depends entirely on the supplier’s system accurately updating the timestamp whenever a product changes. If a supplier’s system doesn’t reliably update timestamps, this method alone can miss genuine changes.

Data-hash comparison

A more robust approach is data-hash comparison. Rather than relying on a timestamp the supplier provides, the system generates its own fingerprint — a hash — of each product’s data on every sync. If a product’s price, stock level, description, or any other tracked field has changed, the hash generated this time will be different from the hash generated last time. If nothing has changed, the hash matches, and the product is skipped.

This method doesn’t depend on the supplier’s timestamp accuracy. It directly compares the actual data, field by field, and only flags a product as changed if something genuinely differs. This is generally the more reliable method, particularly when working with suppliers whose data quality or system behaviour you can’t fully control.

Many systems use a combination of both — timestamp comparison as a fast first check, with data-hash comparison as the more thorough verification.

 

Why “Skipped — No Changes Detected” Is Good News

This is the part that catches retailers off guard the first time they see it at scale. A sync runs. The log shows thousands of products marked “Skipped.” It looks like nothing happened. It looks like a failure.

It isn’t.

“Skipped” means the system checked that product, compared it against the supplier’s current data, found no difference, and correctly decided there was nothing to update. That’s the system doing exactly what it’s supposed to do — confirming the data is current without wasting processing time rewriting information that hasn’t changed.

Think of it like a courier checking every address on their route to see if there’s a parcel to deliver. Most addresses have nothing waiting. That’s not the courier failing at their job — it’s the courier correctly identifying that there’s nothing to deliver there today, and moving on efficiently to the addresses that do have something.

A sync where every product shows “Updated” would actually be more concerning — it would suggest either every single product genuinely changed since the last sync (unlikely for a stable supplier catalogue) or the change-detection logic itself is broken and treating everything as new every time, which defeats the purpose of delta sync entirely and reintroduces the slow, risky full re-import problem it was designed to avoid.

 

What a Healthy Delta Sync Log Actually Looks Like

For a large, relatively stable supplier catalogue, a typical sync log might show:

• Majority “Skipped” — products where the data matches what was processed last time; no action needed
• Small number “Updated” — products where a genuine change was detected (stock level, price, description, or another tracked field) and the update was applied
• Occasional “Created” — new products that appeared in the supplier’s feed for the first time
• Rare “Errors” — products that couldn’t be processed, which genuinely do warrant investigation

The ratio between these categories depends entirely on how frequently the supplier’s catalogue actually changes. A supplier with fast-moving stock and frequent price updates will show a higher proportion of “Updated” entries. A stable, slow-moving catalogue will show mostly “Skipped” — and that’s the expected, healthy pattern, not a sign of a broken connection.

The category worth watching closely is “Errors” — products that couldn’t be matched, processed, or written correctly. That’s the genuine signal that something needs attention, distinct from the routine and expected “Skipped” status that makes up the bulk of any healthy sync.

 

Delta Sync Vs Full Re-Import: A Practical Comparison

Factor	Full re-import	Delta sync
What gets processed	Every product, every time	Only products that have changed
Speed at scale	Slows significantly as catalogue grows	Stays fast regardless of catalogue size
System load	High — full dataset processed every sync	Low — only the changed subset processed
Risk of failure	Higher — more data processed means more opportunity for error	Lower — smaller processing footprint per sync
Sync log appearance	Most products show “Updated” or “Processed”	Most products show “Skipped”
Suited to	Small catalogues, infrequent syncs	Large catalogues, frequent automated syncs

For any retailer running a catalogue in the thousands of SKUs with regular automated syncing, delta sync isn’t an optional optimisation — it’s the difference between a sync process that scales and one that eventually becomes too slow and unreliable to run on the schedule the business actually needs.

 

Why This Matters for Your Retail Operation

The practical business outcome of delta sync isn’t abstract — it directly affects how reliably your Shopify store can stay current with supplier data.

Faster syncs mean you can run them more frequently without the process becoming a bottleneck. More frequent syncs mean your stock and pricing data stays closer to real time, which directly reduces the risk of overselling supplier stock that’s already moved. A sync method that scales with your catalogue means adding more suppliers or more SKUs doesn’t degrade the reliability of the syncs you already depend on.

And understanding what “Skipped” actually means saves your team from chasing a problem that isn’t there — time better spent investigating the genuine errors that do need attention.

 

How Supplier Sync App Uses Delta Sync

The Supplier Sync App uses delta sync as its default import method, combining timestamp and data-hash comparison to identify exactly which products have genuinely changed on each sync run.

This means imports stay fast and reliable regardless of catalogue size — whether you’re managing a few hundred SKUs from one supplier or tens of thousands across several. Import logs clearly distinguish between “Skipped,” “Updated,” “Created,” and “Error” statuses, so your team can see at a glance what actually happened on each sync and focus attention where it’s genuinely needed.

For a full breakdown of how delta sync works in practice — including how to read your import results — the Supplier Sync App User Guide covers the feature in detail.

 

FAQs

What is delta sync in inventory management?

Delta sync is an import method that identifies only the products that have changed since the last sync and updates only those, rather than re-processing an entire catalogue every time. It uses methods like timestamp comparison or data-hash comparison to detect genuine changes, making large-scale syncs significantly faster and more reliable.

Why does my supplier sync log show most products as “Skipped”?

“Skipped” means the system compared that product’s current data against what was processed last time, found no difference, and correctly determined there was nothing to update. For a stable supplier catalogue, most products won’t change between syncs, so a majority “Skipped” result is the expected and healthy outcome — not a sign of failure.

What is the difference between timestamp comparison and data-hash comparison?

Timestamp comparison checks a “last updated” field the supplier provides — if it hasn’t changed, the product is skipped. Data-hash comparison generates a fingerprint of the actual product data on every sync and compares it directly, which doesn’t depend on the supplier’s timestamp accuracy. Data-hash comparison is generally more reliable, particularly when supplier data quality varies.

Is delta sync the same as incremental sync?

Yes, these terms are generally used interchangeably. Both describe an approach where only the changed portion of a dataset is processed on each sync, rather than the entire dataset being reprocessed every time.

When should I be concerned about my sync results?

A high proportion of “Skipped” results for a stable catalogue is normal and expected. What genuinely warrants investigation is a high number of “Errors” — products that couldn’t be matched or processed correctly — or a sync that suddenly shows zero “Updated” results when you know the supplier has made changes, which could indicate a connection issue worth checking.

 

Wondering why your Shopify sync log is full of “Skipped” entries?

The Supplier Sync App uses delta sync to keep large catalogues current — fast, reliable, and only updating what’s actually changed.