Baidu ETL Connector: How MVF Solved an Unsupported Data Source
Why Baidu is a common ETL gap
Baidu is a core advertising and search platform for the Chinese market, but it is rarely supported by off-the-shelf ETL tools.
When a native Baidu ETL connector is unavailable, teams typically end up building and maintaining a custom connector, stitching together manual exports, or operating with partial data:
- Build and maintain a custom pipeline in-house
- Rely on fragile exports or manual uploads
- Accept incomplete reporting for a key channel
All three introduce risk, maintenance overhead, or data blind spots.
For MVF, none of these were acceptable.
MVF’s Baidu ETL challenge
MVF operates a global customer generation platform with paid media activity across multiple regions. Baidu was a required source, not an experiment.
Their existing ETL vendor did not support Baidu. Supporting it internally would have meant more custom jobs, more infrastructure, and more analyst time spent on maintenance rather than insight.
At the same time, MVF was already under pressure from rising ETL costs and fragmented pipelines across their stack.
How MVF implemented a Baidu ETL connector
As part of a wider ETL migration, MVF partnered with Matatika to implement a native Baidu connector.
Instead of treating Baidu as a special case, it was built and operated like any other paid media source:
- Native API-based ingestion
- Integrated scheduling and monitoring
- Consistent schema and reliability standards
- No separate Airflow jobs or parallel infrastructure
The Baidu connector was developed and validated in parallel with existing pipelines, ensuring zero disruption before cutover. Once live, it became part of MVF’s standard ingestion layer rather than an ongoing maintenance burden.
The result
With Baidu fully integrated:
- Reporting coverage expanded without manual workarounds
- Maintenance overhead was eliminated
- Baidu data followed the same refresh and reliability patterns as other paid media sources
- MVF no longer had to check vendor catalogues before adopting new platforms
Baidu joined more than 60 other connectors supporting over 1 billion rows of data annually, all managed under a single, simplified ETL architecture.
The takeaway for teams searching for a Baidu ETL connector
If your ETL stack does not support Baidu, the problem is rarely Baidu itself. It is the rigidity of the connector model.
A usable Baidu ETL connector should not require custom infrastructure, constant fixes, or analyst time to keep running. It should behave like any other core marketing source.
MVF’s experience shows that Baidu does not need to be an exception. It just needs to be treated as infrastructure.
If Baidu is critical to your reporting, your ETL platform should support it directly rather than forcing you to work around it.