Learn more about Reverse ETL from BigQuery to CloudSQL. Our Google Cloud Support team is here to help you with your questions and concerns.

Reverse ETL from BigQuery to CloudSQL | About

Reverse ETL. Short for Extract, Transform, and Load is a process where data is extracted from a data warehouse. Furthermore, It is then transformed as required and then loaded into operational systems or other downstream applications.

Traditionally, ETL is used to move data from operational systems into a data warehouse. Additionally, the data from different source systems are altered and blended into analytical data models.

However, if a user wants to add back the data to their operational system, the data has to be pulled out of the data warehouse, transformed, and then modeled as per the application’s data model. Then, it is loaded into the application database.

According to our experts, we can perform reverse ETL with these steps:

1. First, we have to write a query to extract the required data from our BigQuery tables with BigQuery’s SQL syntax. We can include any necessary joins, filters, aggregations, or transformations in the query.
2. Then, we have to transform the extracted data. Transformation tasks can include formatting, data cleaning, aggregations, or calculations.
3. Next, we can load the transformed data into CloudSQL. We can create a CloudSQL instance and a corresponding database schema that matches the structure of our data. Additionally, we can use appropriate methods or tools to load the data into the CloudSQL database.

Furthermore, a reverse ETL pipeline is not very different from a traditional ETL pipeline. In fact, it only differs in the type of connections used for the target systems.

In other words, while Reverse ETL moves data into end-user applications like ERPs and SaaS applications, ETL helps move data from traditional OLTP databases into data warehouses and lakes.

How to automate Reverse ETL

Now, let’s take at different ways to automate the process:

• Scheduled jobs
• Event-driven triggers
• ETL tools like Apache Airflow, Google Cloud Dataflow, etc.

Our experts recommend choosing an approach or tool based on the complexity of the data, the volume of data to be processed, specific requirements for data integration and synchronization between BigQuery and CloudSQL, and so on.

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Conclusion

To conclude, our Support Techs gave us a quick look at reverse ETL from BigQuery to CloudSQL and how to automate the process as well.