You can migrate from an existing web scraping vendor without losing historical data, as long as you export everything before your contract ends, preserve schema and record identifiers, and run both vendors in parallel until the new feed is verified.
The only risk is treating it as a simple vendor swap instead of a data continuity project.
Most failed migrations fail because the old dataset gets orphaned: field names change, product IDs don’t match across vendors, or the historical archive was never exported before access was cut off. Once that happens, trend lines break and dashboards show gaps that take months to explain.
The fix is procedural. Before switching:
- Export historical data and metadata from the current vendor
- Preserve schema and field definitions in the new setup
- Keep stable record identifiers (SKU, ASIN, GTIN, product URL) so old and new data merge cleanly
- Run both vendors simultaneously for a defined overlap period
- Validate data quality before the final cutover
Continue reading to know each step in detail
Step 1: Export Everything Before Ending Your Existing Contract
Once a contract ends, access to the old vendor’s system, dashboard, or API usually ends with it. Export before you cancel, not after.
Raw Scraped Data
Pull the full historical dataset, not just the most recent snapshot. Export in a format your pipeline can already ingest (CSV, JSON, or direct database dump) so nothing needs to be reverse-engineered later.
Metadata
Raw records aren’t enough on their own. Export the metadata that gives them context:
- Timestamps for each record
- Crawl times and crawl frequency
- Source URLs
- HTTP status codes
- Crawl logs and error logs
Without this metadata, it’s difficult to tell whether a gap in the data is a real market event or just a failed crawl.
Documentation
Get a copy of the schema, field definitions, and any API documentation before the relationship ends. This documentation is often the only reference for how a field like availability or price_type was actually defined, and it disappears once the account is closed.
Step 2: Preserve the Existing Data Schema
Changing field names during a migration seems minor, but it creates unnecessary downstream work for every system that reads that data.
For example, if the current schema uses:
- Product_name
- Price
- Currency
- Availability
Avoid renaming them to something like:
- Title
- Cost
- Curr
- Stock
Even though the values might be identical, every ETL pipeline, BI dashboard, internal analytics query, API integration, and machine learning feature set that references the old field names will break or silently misread the new ones. Schema drift is one of the most common causes of “the data looks wrong” tickets after a vendor switch, even when the underlying data is accurate.
If the new vendor’s default schema differs from the old one, request a schema mapping layer that outputs data in your existing field structure. This keeps every downstream system working without a rebuild.
Step 3: Keep Stable Record Identifiers
Field names are one part of continuity. Record identifiers are the other, and they matter more.
Historical and new records only merge correctly if they share a stable identifier across vendors, such as:
- Product IDs
- SKU
- Retailer product URL
- ASIN
- GTIN
- Business or listing IDs
If the new vendor assigns its own internal IDs instead of preserving the retailer’s native identifiers, historical records and new records become two disconnected datasets that can’t be joined without manual matching. Before migration, confirm which identifier the new vendor uses as the primary key and make sure it matches what’s already in the historical archive.
Step 4: Run Both Vendors in Parallel
Don’t switch on a single date. Run both vendors side by side for a defined window and compare:
- Outputs: do both vendors return the same values for the same products?d
- Coverage: does the new vendor reach the same set of pages, categories, or retailers?
- Freshness: how current is each vendor’s data relative to the live site?
- Missing records: are there products the old vendor captured that the new one misses, or vice versa?
- Error rates: how often does each vendor fail, time out, or return partial data?
Overlap providers for 2 to 4 weeks before fully switching, long enough to catch discrepancies tied to weekly or monthly cycles, but short enough to avoid paying double for an extended period. If your data has a strong monthly cadence (like inventory resets or promotional cycles), consider extending the overlap to a full cycle.
Step 5: Validate Data Quality Before the Final Cutover
Before turning off the old vendor, run through a data validation checklist against the new feed:
- Record counts: Does the new vendor return a comparable volume of records?
- Missing fields: Are any expected fields null or absent that were previously populated?
- Duplicate records: Is the new vendor introducing duplicates that weren’t there before?
- Freshness: Is data refreshed on the same schedule your systems expect?
- Price accuracy: Spot-check prices against the live site
- Image URLs: Confirm image links resolve and aren’t broken or region-locked
- Category mappings: Check that category and subcategory labels match your taxonomy
- Geographic coverage: Verify the new vendor covers the same regions, locales, or store locations
Only after this checklist passes should the old vendor be switched off.
Step 6: Reconcile Historical and New Data
Merging the archive with the new feed needs a few deliberate rules, not just a database join.
- Append-only history: treat the historical archive as immutable. New data gets appended, not overwritten, so nothing from the old vendor is lost in the merge.
- Deduplication: define a clear rule (usually based on record ID and timestamp) for handling any overlap period where both vendors captured the same record.
- Timestamp normalization: align time zones and timestamp formats between vendors before merging, or trend lines will show artificial jumps.
- Handling changed product URLs: retailers change URL structures over time. Map old URLs to new ones where possible instead of treating them as new products.
- Handling discontinued products: don’t drop discontinued items from the historical dataset. Mark them as inactive so historical trend analysis stays intact.
Common Migration Challenges (and How to Avoid Them)
Different Schemas
Vendors rarely use identical field names or data structures. Avoid this by requesting a schema mapping layer upfront rather than rebuilding pipelines around the new vendor’s default output.
Missing Historical Exports
Some vendors don’t retain long-term archives, or make exports difficult after a contract ends. Avoid this by exporting historical data before initiating the cancellation process, not after.
Different Update Frequencies
If the old vendor refreshed data daily and the new one refreshes weekly (or vice versa), your dashboards and models will see a change in data density that has nothing to do with the market. Confirm refresh cadence during evaluation, not after signing.
Different Anti-Bot Approaches Causing Coverage Differences
Two vendors scraping the same website can return different coverage depending on how each handles anti-bot measures. Avoid surprises by comparing coverage directly during the parallel run, not by assuming equivalent capability.
Vendor-Specific APIs
Custom or proprietary API structures can lock your pipeline into vendor-specific logic. Avoid this by requesting standard delivery formats (CSV, JSON, or a common schema) wherever possible, so the underlying vendor becomes easier to change again in the future.
Best Practices for a Zero-Downtime Migration
- Never terminate the old vendor before the new one is fully validated
- Freeze schema changes on both sides during the migration window
- Version your data pipelines so you can roll back to a prior state if needed
- Back up historical datasets independently of either vendor’s systems
- Validate the new feed against production-quality checks before it replaces the live feed
- Keep rollback capability active until at least one full business cycle has passed on the new vendor
When It Makes Sense to Switch Vendors
Migration is worth the effort when the switch solves a real, recurring problem rather than a one-off complaint. Common reasons companies switch include:
- Better data quality or accuracy
- Larger coverage across retailers, regions, or categories
- Faster delivery and lower latency
- Lower costs at comparable quality
- Better support and faster issue resolution
- Custom scraping needs the current vendor can’t accommodate
- AI-ready, structured datasets suited for LLM or ML pipelines
- Stronger SLAs around uptime and data freshness
None of these reasons make migration risk-free. They just make it worth planning for properly.
How ScrapeHero Makes Vendor Migration Easier
Vendor migrations go smoothly when the new provider treats continuity as part of the deliverable, not an afterthought. ScrapeHero works with companies switching from another scraping vendor or an in-house scraper with this in mind.
Historical Data Compatibility
ScrapeHero can ingest and align historical exports from a prior vendor, so existing archives stay usable alongside new data instead of becoming a separate, disconnected dataset.
Schema Matching
Rather than forcing a new schema onto existing pipelines, ScrapeHero maps output to match your current field names and structure, so downstream ETL, BI tools, and models keep working without a rebuild.
Parallel Onboarding
ScrapeHero supports running alongside an existing vendor during the transition period, so you can validate coverage, freshness, and accuracy before switching anything off.
Custom Data Validation
Validation checks (record counts, field completeness, duplicate detection, freshness) are set up around your specific data quality requirements, not a generic template.
Dedicated Migration Support
A migration involves more than a data feed. ScrapeHero assigns dedicated support through the transition to handle schema questions, coverage gaps, and edge cases as they come up.
Flexible Delivery Formats
Data can be delivered in the format your systems already expect: CSV, JSON, direct APIs, or delivered straight into Snowflake, BigQuery, Amazon S3, Azure Blob Storage, or FTP.
The goal in a migration isn’t to prove one vendor is better than another. It’s to make the switch invisible to everything downstream of the data.
FAQ
Can I migrate web scraping vendors without losing historical data?
Yes. This requires exporting the full historical archive and metadata before the old contract ends, preserving schema and field definitions, keeping stable record identifiers across vendors, and validating the new feed before cutover.
How long should I run two scraping vendors simultaneously?
Most organizations run both vendors in parallel for 2 to 4 weeks. If your data has a monthly or seasonal cycle, extending the overlap to cover a full cycle gives a more reliable comparison.
What data should I export before switching vendors?
Export the raw historical dataset, associated metadata (timestamps, crawl times, source URLs, status codes, crawl logs), and documentation (schema definitions, field definitions, API documentation) before ending the existing contract.
