Delta Chapter 6: Time Travel (Long Quiz)(Multiple Choice)

36 Questions

What is the purpose of the TABLE_CHANGES SQL command?

To view changes and CDF metadata columns

What happens if the 'end' argument is not specified in the TABLE_CHANGES command?

All changes from the start up to the current change are returned

What is the purpose of the 'table_str' argument in the TABLE_CHANGES command?

To represent the optionally qualified name of the table

What is the significance of the '_commit_timestamp' column in the output of the TABLE_CHANGES command?

It indicates the timestamp of the change

What is the purpose of the 'ORDER BY _commit_timestamp' clause in the SQL command?

To sort the results in ascending order of timestamp

What can an event-streaming platform like Kafka do with the change feed from a Delta table?

Trigger near-real-time actions in a downstream application or platform

What is the primary purpose of the Change Data Feed (CDF) in Delta Lake?

To provide a full audit trail of data changes

How can you enable the Change Data Feed for all new tables in Delta Lake?

By setting a Spark configuration property

What is an example of an event-driven application that can benefit from the Change Data Feed?

An e-commerce platform

What is the main advantage of using the Change Data Feed over time travel in Delta Lake?

Enhanced efficiency

What is the purpose of the audit trail table in Delta Lake?

To provide a full audit trail of data changes

What can you specify when creating a table or altering an existing one to enable the Change Data Feed?

A table property

What is a key consideration when using the RESTORE operation in Delta Lake?

It can affect downstream jobs such as Structured Streaming jobs.

What happens when the RESTORE operation is used to restore a Delta table to a previous version?

The table is restored to its previous version, and previous updates are processed again.

What is the purpose of the OPTIMIZE operation in Delta Lake?

To remove files related to older versions of the table.

What is the result of the RESTORE operation in Table 6-1?

The table is restored to version 3, and files related to versions 1 and 2 are added.

What is the effect of the dataChange parameter in the RESTORE operation?

It determines whether the operation affects downstream jobs or not.

What is the purpose of the Delta log in Delta Lake?

To track changes to the table data.

What is a consequence of using the RESTORE operation with dataChange = true?

The table is restored to its previous version, and previous updates are processed again by the streaming job.

What is the result of the OPTIMIZE operation in Table 6-1?

Files related to versions 1 and 2 are removed.

What does the _commit_version indicate in the row-level changes?

The versions that correspond to when a particular record was inserted, updated, or deleted

What does the _change_type indicate in the row-level changes?

The type of operation on the record

What is the purpose of the update_postimage in the row-level changes?

To indicate the row-level data after the update

What is the method used to view table changes using the DataFrame API?

Using the .option() method and setting 'readChangeFeed' to 'true'

What is the purpose of the 'startingVersion' and 'endingVersion' options in the DataFrame API?

To specify the range of versions to view table changes

What is the alternative to specifying versions when viewing table changes using the DataFrame API?

Specifying timestamps

What is the purpose of the TABLE_CHANGES() function in the context of Changing Data Feed?

To view the audit trail of a record and see how it has changed over time

What is the advantage of using the Changing Data Feed (CDF) to capture the audit trail of a record?

It is more efficient than other methods

What is the primary purpose of the query shown in the screenshot?

To provide an audit trail of data updates

What is the significance of the `_change_type` column in the query?

It shows the type of change made to the data

What is the advantage of using the `table_changes` function over traditional time travel methods?

It is more efficient and scalable

What is the granularity of the `tripAggregates` table?

Vendor ID only

What is the purpose of the `WHERE Vendorld = 1` clause in the query?

To select only the data for vendor ID 1

What does the `ORDER BY _commit_timestamp` clause do?

It sorts the data in ascending order by commit timestamp

What is the purpose of the second query shown in the text?

To count the number of new vendors added since a certain date

What does the `SELECT *` statement do in the queries?

It selects all columns from the table

Study Notes

RESTORE Considerations and Warnings

RESTORE is a data-changing operation, meaning it can potentially affect downstream jobs, such as Structured Streaming jobs.
RESTORE can lead to re-processing of previous updates to a Delta table by a streaming job, since the transaction log restores previous versions of the data using the add file action with dataChange = true.
The streaming job recognizes the records as new data, potentially causing duplicate processing.

Operations Resulting from RESTORE

Table version 0: INSERT operation with AddFile action and dataChange = true.
Table version 1: INSERT operation with AddFile action and dataChange = true.
Table version 2: OPTIMIZE operation with AddFile and RemoveFile actions, and dataChange = false.
Table version 3: RESTORE operation with RemoveFile and AddFile actions, and dataChange = true.

Change Data Feed (CDF)

The CDF provides an efficient way to track changes to row-level data over time.
It enables querying of changes to row-level data, providing a full audit trail of data.
The CDF is essential for regulatory requirements, such as HIPAA, to track changes to electronic protected health information (ePHI).
Enabling the CDF for all new tables can be done by setting the Spark configuration property spark.databricks.delta.properties.defaults.enableChangeDataFeed to true.
The CDF can be enabled for specific tables using table properties when creating or altering a table.

TABLE_CHANGES() SQL Command

The TABLE_CHANGES command allows viewing changes to a table and its CDF metadata columns.
The command takes three arguments: table_str, start, and end.
table_str is the optionally qualified name of the table.
start is the first version or timestamp of change to return.
end is an optional argument for the last version or timestamp of change to return.

Using TABLE_CHANGES() Command

The TABLE_CHANGES command can be used to view row-level changes to a table, including insert, update, and delete operations.
The command returns the _change_type and _commit_version columns, which indicate the type of operation and the version of the change.
The command can be used to view changes to a specific table or vendor, and to track changes over time.

Audit Trail and Time-Series Analysis

The CDF can be used to create an audit trail of changes to a specific record or vendor over time.
The CDF can be used for time-series analysis, such as tracking the addition of new vendors and their fare amounts over time.
The CDF provides an efficient way to query changes to row-level data, making it a powerful tool for auditing and analytics.

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