Chapter 1: Understanding Change History in BigQuery

When transferring data from source systems into Google BigQuery, whether through ETL or ELT processes, it's essential to monitor and log any INSERT, UPDATE, or DELETE actions within a table. This article outlines how to effectively implement versioning in BigQuery.

As mentioned in the referenced article, establishing versioning is crucial for tracking modifications. This allows for the creation of meaningful views that display only the most recent data records, as well as the capability to maintain historical records, which can be particularly beneficial for applications like fraud detection in finance. A Data Vault structure, for example, can be utilized for this purpose.

Now, with BigQuery's change history feature, you can monitor the modifications made to a BigQuery table. This functionality is available as a SQL table-valued function (TVF) that outlines specific changes occurring over a designated timeframe. Understanding these changes allows for more efficient incremental maintenance of table replications outside of BigQuery, minimizing the need for costly table duplications.

This new feature streamlines various processes since Google automates much of the change tracking, enabling you to perform SQL queries to retrieve this information effortlessly.

Here's a basic example:

CREATE TABLE Data.Test (ID INT64, Name STRING) AS (

SELECT 1 AS ID, ‘ANA’ AS Name);

Next, let’s add some entries to this table:

INSERT INTO Data.Test

VALUES(2, 'Tim');

To query the history of this table, you can use the following SQL command:

SELECT

ID,

Name,

_CHANGE_TYPE AS change_type,

_CHANGE_TIMESTAMP AS change_time

FROM

APPENDS(TABLE DATA.Test, NULL, NULL);

The output will resemble this:

This remarkable feature from Google simplifies the data integration process significantly, potentially eliminating the need for complex historical tracking logic. If you're intrigued by this topic, you might also find the following article beneficial: "How to Access Historical Data using Time Travel in BigQuery".

Chapter 2: Additional Resources on BigQuery