Solved: BigQuery - incrementable tables - Page 2

ayushmaheshwari · 11-24-2023 02:07 PM

I am currently running a pipeline through Airflow every 12 hours. Each time the pipeline is run, the BigQuery table is rewrittem (WRITE_TRUNCATE). This is working fine.

However, I want to append only the new data into the table. Instead of using WRITE_APPEND can I use another means to append the table. LAst tme I used WRITE_APPEND, I fgaced issues as the table was populated with repated rows in many cases.

Can I have a timestamp column (current_timestamp) so that after the time stamp the rows get appended into the BigQuery table?

ms4446

Based on your use case of ingesting data from a BigQuery table in Project X into your table in Project Y, here's an enhanced approach to ensure efficient and accurate data transfer:

Ingesting New Data from BigQuery Table in Project X to Project Y

Identifying New Data:
- Preferably use a timestamp column if available in Project X's table.
- If a timestamp column is absent, identify new data using alternative methods like the highest ID or a unique key.
Loading New Data into a Staging Table:
- Create a staging table in Project Y.
- Write a query to select data from Project X's table, focusing on rows that are newer than those in your last update.
- Load these filtered results into the staging table in Project Y.
Transferring Data to Main Table (Table B):
- Utilize a MERGE statement to efficiently transfer data from the staging table to Table B in Project Y.
- Design the MERGE statement to avoid duplicates, typically by matching on a unique identifier.

Simplified Example:

-- Query to select new data from Project X's table
INSERT INTO ProjectY.staging_table
SELECT *
FROM ProjectX.TableA
WHERE timestamp_column > (
  SELECT MAX(timestamp_column)
  FROM ProjectY.TableB
);

-- Merge statement to insert new data into your main table
MERGE INTO ProjectY.TableB
USING ProjectY.staging_table
ON ProjectY.TableB.id = ProjectY.staging_table.id
WHEN NOT MATCHED THEN INSERT (columns...)
VALUES (values...);

-- Optionally, clean up the staging table

Additional Considerations:

Incremental Data Loading: Implement incremental loading for efficiency, especially for large datasets.
Error Handling: Develop robust error handling mechanisms within your pipeline.
Data Integrity Checks: Perform checks post-transfer to ensure data accuracy and completeness.
Scheduling: Automate and schedule your pipeline runs in alignment with the data update patterns in Project X.
Documentation and Maintenance: Maintain up-to-date documentation for your data pipeline for ease of maintenance and future modifications.
Compliance and Security: Adhere to compliance and security protocols, especially when handling sensitive data.

This enhanced approach should provide a robust framework for transferring only new data from Project X to Project Y, ensuring data integrity, efficiency, and compliance with security standards.

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