Snowflake - Build and Architect Data Pipelines Using AWS - Streams - Data Retention and Staleness

The video tutorial explains the concept of staleness in data streams and introduces time travel in Snowflake. It covers how data retention policies affect stream staleness and provides insights into billing and data storage. The tutorial also touches on Snowflake's internal versioning system and concludes with a brief mention of change tracking.

What is the primary purpose of time travel in Snowflake?

• To travel back in time physically

• To access historical data snapshots

• To increase data retention period

• To reduce data storage costs

What happens to a stream if it is not consumed within the data retention period?

• It is automatically deleted

• It is refreshed

• It becomes stale

• It is archived

How does Snowflake prevent streams from becoming stale if the data retention is less than 14 days?

• By deleting the stream

• By extending the data retention period temporarily

• By refreshing the stream

• By archiving the stream

What is the main cost associated with streams in Snowflake?

• Stream creation

• Data retention policy

• Virtual warehouse processing time

• Data storage

Do streams in Snowflake store data?

• Yes, they store all data changes

• No, they rely on internal versioning

• Yes, but only for 14 days

• No, they store data temporarily

What does Snowflake use to track changes in streams?

• Internal versioning system

• Manual tracking

• External databases

• Data logs

What is an offset in the context of Snowflake streams?

• A pointer that tracks data changes

• A method to delete old data

• A tool for data encryption

• A way to increase data retention