BigQuery Salesforce Integration: Getting Your Data Where It Needs to Be
Source: Dev.to
The Real Challenge
Most articles focus on getting data from Salesforce into BigQuery for analysis. That’s the easy direction. What’s harder—and what you actually need—is pushing data from BigQuery into Salesforce and then tracking what happens to it. You need to know if the data actually made it, if Salesforce accepted all the records, and what to do with records that failed.
When you’re dealing with massive amounts of data sent in batches, there’s always a risk that the number of records sent doesn’t match what was received. Manually checking row counts between systems isn’t viable. You need automation that handles the transfer, validates the results, and manages exceptions.
The Technical Approach
You’ll use the Simple Salesforce library, a REST API client built for Python. It handles the connection to Salesforce and provides methods for inserting, updating, and querying data. This library is well‑maintained and widely used.
Validation and Error Handling
- Collect the IDs of records you sent.
- Query Salesforce to retrieve those records and confirm they were created or updated correctly.
- Load the response data back into a temporary BigQuery table, which serves as your validation layer.
By comparing what you sent with what Salesforce confirms it received, you can identify records that failed to load. Reasons may include validation rule violations, data type mismatches, or API limits. The resulting list of failed records can be logged, corrected, and resubmitted automatically. This closed‑loop process ensures data integrity without manual intervention.
Automation with Airflow
Airflow provides scheduling, monitoring, and alerting, so you know immediately if something fails. It handles retries, logs execution history, and gives you visibility into your data pipeline that you simply don’t get with manual processes or black‑box integration tools.
Why This Approach Works
- Maintainable: The technology stack (Python and Airflow) is standard and widely used, making it easy to find developers who can maintain and enhance the solution.
- Scalable: Both BigQuery and Salesforce APIs can handle high volumes. The Bulk API processes thousands of records efficiently, and Airflow can orchestrate multiple parallel workflows if needed.
The Implementation Reality
The Partnership Advantage
A knowledgeable partner can implement the solution faster than an internal team learning as they go. They will set up proper error handling, logging, and monitoring from the start rather than adding it after problems emerge, and they will document the solution so your team can maintain it going forward.
Moving Forward
Proper implementation by experienced professionals is essential. Don’t underestimate the complexity, but also don’t be intimidated. With the right technical partner, you can achieve automated, reliable BigQuery‑Salesforce integration that keeps your sales team working with current, accurate data.