How to use our current Python ETL pipelines to populate and keep BigQuery tables in sync?

[faele]

How to use our current Python ETL pipelines to populate and keep BigQuery tables in sync?

• We have some ETL pipelines in Python that we use to populate a db (PostgresSQL) that we use for our reports (currently using Looker Studio).
• We want to direct some of these towards BigQuery to evaluate the possible replacement of Postgres with BigQuery.
• We do UPSERT operations every hour and every day we resynchronize the last day.
  Furthermore, it may happen that we have to resynchronize the database from the beginning of the year.
• Writing direct BigQuery upsert queries would be particularly expensive (every time the entire table size).
• We realized that Analytics historicizes by creating a subtable for each day.
• Can we use the same approach to connect our current ETL pipelines and update only 1 day?

[ms4446]

Yes, you can use a similar approach of creating subtables for each day to connect your current ETL pipelines and update only one day in BigQuery. This method is indeed more efficient than performing full table upserts, as it minimizes the amount of data that needs to be processed and written each time.

Here's a step-by-step approach to implementing this solution:

1. Create a Daily Staging Table: Establish a staging table in BigQuery for each day's data. This table will temporarily hold the data that needs to be upserted into the main table. Ensure that these staging tables are structured to facilitate efficient data loading and merging.

2. Partition the Main Table: Implement partitioning on the main table in BigQuery by date. This strategy is crucial for managing large datasets, as it allows for more efficient querying and data manipulation, particularly for time-based data.

3. Update the Staging Table: Adapt your existing ETL pipelines to populate the daily staging tables, ensuring compatibility with BigQuery's API and data loading methods. This may involve modifying your Python scripts to interface correctly with BigQuery.

4. Upsert Data from Staging Table to Main Table: Use BigQuery's MERGE statement to perform upsert operations from the staging table to the corresponding partition of the main table. This should be done carefully to update only the data for the specific date, thereby optimizing performance and cost.

5. Delete Staging Table: After the upsert operation, clean up by deleting the daily staging table. This step is important for managing storage costs and maintaining a tidy data environment. However, consider retaining these tables temporarily if they might be needed for audit purposes or reprocessing in case of errors.

Additional Enhancements:

• Testing and Validation: Prior to full-scale implementation, conduct thorough testing with a subset of your data. This ensures the process works as expected and maintains data integrity.

• Monitoring and Optimization: Continuously monitor the performance and costs of your BigQuery operations. Utilize BigQuery's tools for query optimization and cost management to maintain an efficient and cost-effective environment.

• Error Handling and Recovery: Develop robust error handling mechanisms in your ETL pipelines. This includes strategies for dealing with data load failures, transformation errors, and other unexpected issues.

• Documentation and Training: Update your operational documentation to reflect these new processes. Provide training and resources to your team members to facilitate a smooth transition to the new system.

• Compliance and Security: Ensure that your data handling and storage practices in BigQuery comply with all relevant data privacy and security regulations.