Snowflake Time Travel & Fail-safe | What is Continuous data protection | How to | with Examples

Summary

In this video, Snowflake's key data protection features—Time Travel and Fail Safe—are explored in depth. Time Travel allows users to recover table data from the past, including reverting accidental changes or restoring deleted objects, while Fail Safe provides an additional 7-day recovery period with Snowflake support if data falls outside the Time Travel window. The video also explains the role of Snowflake’s micro-partitioning system in supporting these features, including how data is stored and modified. Viewers gain a comprehensive understanding of how to use these tools to protect and recover critical data efficiently.

Takeaways

• 😀 Time Travel in Snowflake allows users to revert data to a specific point in the past, recovering from accidental changes or deletions.
• 😀 Snowflake's data protection mechanisms include both Time Travel and Fail Safe, providing multiple layers of protection against data loss.
• 😀 Time Travel works by storing data in immutable micro partitions, where updates are handled by marking the old partition with an end date and creating a new one with the updated data.
• 😀 Snowflake offers up to 90 days of Time Travel retention, depending on the configuration, allowing flexible recovery windows for different use cases.
• 😀 The Fail Safe period lasts for 7 days after the Time Travel window, providing additional recovery time through Snowflake support, though direct self-recovery is not possible.
• 😀 With Time Travel, you can recover dropped tables, schemas, or databases using the 'UNDROP' statement, making recovery easy after accidental deletions.
• 😀 You can query data at a specific time in the past by using exact timestamps or offsets, and also by utilizing the query ID from DML operations to restore the table state before a specific change.
• 😀 Retaining micro partitions beyond their end date incurs additional storage costs, so it's important to manage retention periods carefully based on your needs.
• 😀 Snowflake’s Time Travel feature enables cloning of tables, schemas, and databases as they existed in the past, providing a powerful tool for managing historical data.
• 😀 To recover data after the Time Travel window has passed, the Fail Safe process requires submitting a support case, followed by feasibility assessment and script execution provided by Snowflake support.

Q & A

What are the two key data protection features discussed in the video?

The two key data protection features discussed in the video are Time Travel and Fail Safe.

How does Snowflake's Time Travel feature work?

Time Travel allows you to recover table data as of specific dates or times in the past. You can revert accidental or incorrect DML operations, or even recover a dropped table, schema, or database within the retention window.

What is the role of micro-partitions in Snowflake’s data protection features?

Micro-partitions are immutable data chunks used for storage in Snowflake. When data is modified, Snowflake creates a new micro-partition, leaving the old one intact. This enables features like Time Travel and Fail Safe by preserving data history.

How long is the Fail Safe period in Snowflake?

The Fail Safe period in Snowflake lasts for 7 days. During this period, data recovery is only possible through Snowflake support.

What happens when a record is updated in Snowflake's micro-partitioning model?

When a record is updated, Snowflake sets an end date for the existing micro-partition and creates a new partition with the updated data. This process enables Time Travel and Fail Safe features.

How can you recover data using Time Travel after making a DML change?

You can recover data using Time Travel by querying a historical snapshot of the table at a specific timestamp or using an offset to move back by minutes, hours, or even days. You can also use the query ID from DML operations to recover data from before the operation.

What is the difference between columnar and row-oriented storage in Snowflake?

Columnar storage in Snowflake stores data in columns, which allows for better compression and faster queries. In contrast, row-oriented storage stores data in rows, which is less efficient for analytical queries.

Can you clone a table in Snowflake to a previous point in time?

Yes, you can clone tables, schemas, and databases in Snowflake as they existed at a specific point in the past using Time Travel.

What should you do if you accidentally drop a table, schema, or database in Snowflake?

If you accidentally drop a table, schema, or database, you can use Snowflake's Undrop feature to restore it to its previous state using SQL statements.

How can you configure the Time Travel retention period in Snowflake?

You can configure the Time Travel retention period in Snowflake up to 90 days, depending on your requirements. This can be done using SQL statements to adjust the retention settings for individual tables.