Snowflake - Build and Architect Data Pipelines Using AWS - Lab - Change Tracking Using "Changes"

The video tutorial explains change tracking as a read-only alternative to streams in databases. It covers the implementation of change tracking, including enabling it on tables and querying changes using SQL. The tutorial also compares change tracking with streams, highlighting when to use each method based on data update frequency and consumption needs. The video concludes with a brief mention of building a complete data pipeline.

What is the primary purpose of enabling change tracking on a table?

• To improve query performance

• To create a backup of the table

• To enable read-only access to change data

• To allow direct data modification

Which SQL function is used to track changes in a table?

• CHANGES

• SELECT

• UPDATE

• ALTER

What does the 'default' parameter in the 'changes' function return?

• Only deleted rows

• Only updated rows

• Both newly ingested and updated rows

• Only newly ingested rows

How can you view only the inserted rows using the 'changes' function?

• Set the parameter to 'update only'

• Set the parameter to 'default'

• Set the parameter to 'pen only'

• Set the parameter to 'insert only'

What is the main advantage of using change tracking over streams?

• It does not require a defined frequency for consuming change data

• It requires less storage space

• It automatically updates the production table

• It allows for frequent updates

When should you prefer using streams over change tracking?

• When you do not need to track changes at all

• When the table is updated infrequently

• When you need to consume change data at arbitrary intervals

• When the table changes frequently and you need real-time reporting

What is a key characteristic of change tracking?

• It advances the offset automatically

• It is a write-only process

• It is a read-only alternative to streams

• It requires creating a stream with an explicit transactional offset