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Questions and Answers
What does the _delta_log folder contain in managed tables?
How can a previous version of a table be restored in Delta Lake?
What SQL command is used to reduce the size of log files in Delta Lake by combining records?
What is the purpose of the VACUUM command in Delta Lake?
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What does the ZORDER option do when optimizing a table in Delta Lake?
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Which command will prevent the deletion of files older than 7 days during the VACUUM process?
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What is required to view the transaction history of a Delta table?
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What happens when the RESTORE command is executed in Delta Lake?
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What is the main difference between 'Create or replace as' (CRAS) and 'Insert Overwrite'?
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Which of the following statements is true regarding the Insert Overwrite operation?
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What is a notable benefit of using the merge or upsert statement?
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How can duplication be avoided when using the merge operation?
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What are the expectations for the Copy Into operation in Databricks?
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Which operation is potentially cheaper for incrementally ingesting data according to the expected behaviors?
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What is a key feature of the merge statement in terms of database operations?
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Which statement accurately reflects the limitations of Insert Overwrite?
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Which command can be used to check the metadata of an external table?
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What is a key characteristic of User-Defined Functions (UDFs) in Databricks?
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To drop duplicates from a DataFrame, which Python command is correct?
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Which statement correctly formats a date from a timestamp column in SQL?
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When reading a CSV file into Databricks, which option can specify that the CSV has a header?
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What aspect of Delta Lake ensures that all transactions are atomic?
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In Databricks, which of the following is NOT a method of dealing with null values?
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Which of the following SQL commands can create an external table with CSV data?
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In the context of Delta Lake, what does cloning refer to?
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When formatting date and time in Python using DataFrame, which command correctly formats the time column?
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Study Notes
Databricks Delta Lake
- Delta Lake allows for versioning of data in a table.
- Versions are stored in a folder called
_delta_log
- Each version has a specific timestamp and version number.
- You can query a specific version of a table using the
TIMESTAMP AS OF
andVERSION AS OF
clauses. - You can restore a table to a previous version using
RESTORE TABLE
and specifying the timestamp or version number. - This process is known as time-traveling.
- Restore creates a new version in the table's history.
- For improved retrieval, you can optimize a table by combining records and rewriting the results.
- The
OPTIMIZE
command can be used for this. - You can also include a
ZORDER
clause to create an index for fast retrieval. - Delta Lake uses a vacuum process to clean unused and old files .
- This helps reduce storage costs and enforce retention policies.
- The
VACUUM
command deletes files older than a specified time. - The
RETAIN
option can be used to specify a retention period, default is 7 days. - Premature deletion can be prevented by disabling the retention duration check.
-
DRY RUN
option is used to print all records to be deleted without performing the vacuum operation.
Overwriting Tables
- Two methods for overwriting tables: Create or Replace as (CRAS) and Insert Overwrite.
- The
CREATE OR REPLACE TABLE
statement creates a new table if one doesn't exist or replaces the existing table. - Insert overwrite overwrites an existing table using a new data source.
- Insert overwrite can only overwrite existing tables, not create new tables.
- Insert overwrite requires new records to match the table's schema.
- It can overwrite individual partitions, enforcing the same schema.
Merge
- The
MERGE
statement supports inserting, updating, and deleting records using a single transaction. - Enables the implementation of custom logic with extensive options.
- Syntax includes
WHEN MATCHED
andWHEN NOT MATCHED
clauses for applying update and insert operations. - Merge can be used to avoid duplicate records by only inserting new records using a single transaction, using
WHEN NOT MATCHED
. - For incremental data ingestion, the
COPY INTO
command is used, making it efficient for large datasets. - Data schema consistency and handling of duplicates are essential considerations.
Query Files
- Data files can be queried directly in SQL.
- Supported file formats include:
FILE_FORMAT
,TEXT
,JSON
,CSV
, andBINARY_FILE
. - Each format can be specified in the SQL statement.
External Tables
- External tables allow access to external data sources without copying it into Databricks.
- They are defined using a
CREATE TABLE
statement with the specific file format and options. - External tables can be described using
DESCRIBE EXTENDED
and refreshed usingREFRESH TABLE
. - External tables can be created using different methods (e.g., CSV, JDBC, JSON).
Data Cleaning
-
COUNT_IF
andCOUNT
withWHERE
conditions can be used to identify null values. -
DISTINCT
can be used to remove duplicate records. - Date formatting can be achieved with
date_format
andregexp_extract
functions. - Custom column transformations can be achieved using user-defined functions (UDFs).
User-Defined Functions
- UDFs provide a way to create custom logic for transforming data.
- UDFs cannot be optimized by the Spark Catalyst Optimizer.
- UDFs are serialized and sent to executors, resulting in overhead.
-
udf()
function registers a UDF for use in DataFrame transformations. - UDFs are a powerful tool for custom data manipulation and transformations.
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Description
This quiz explores the functionalities of Databricks Delta Lake, including data versioning, time-traveling, and optimization techniques. Learn how to use commands like RESTORE
, OPTIMIZE
, and VACUUM
for effective data management and retrieval. Test your knowledge on enhancing data operations within the Delta Lake environment.