Databricks Data Engineering with Delta Lake
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Databricks Data Engineering with Delta Lake

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Questions and Answers

What does the _delta_log folder contain in managed tables?

  • Backup copies of the data
  • Compressed version of the data files
  • Temporary staging files for uploads
  • JSON files with transaction history (correct)
  • How can a previous version of a table be restored in Delta Lake?

  • Using the RESTORE command with table name (correct)
  • Using the VACUUM command with a timestamp
  • With the OPTIMIZE command specifying version number
  • By executing the DROP command first
  • What SQL command is used to reduce the size of log files in Delta Lake by combining records?

  • VACUUM
  • CREATE TABLE
  • OPTIMIZE (correct)
  • RESTORE
  • What is the purpose of the VACUUM command in Delta Lake?

    <p>To clean up unused and old files</p> Signup and view all the answers

    What does the ZORDER option do when optimizing a table in Delta Lake?

    <p>It creates indexes based on specified columns</p> Signup and view all the answers

    Which command will prevent the deletion of files older than 7 days during the VACUUM process?

    <p>SET spark.databricks.delta.retentionDurationCheck.enable = true;</p> Signup and view all the answers

    What is required to view the transaction history of a Delta table?

    <p>Running DESCRIBE HISTORY on the table</p> Signup and view all the answers

    What happens when the RESTORE command is executed in Delta Lake?

    <p>It adds a new version to the table's history</p> Signup and view all the answers

    What is the main difference between 'Create or replace as' (CRAS) and 'Insert Overwrite'?

    <p>CRAS can create a new table while Insert Overwrite cannot.</p> Signup and view all the answers

    Which of the following statements is true regarding the Insert Overwrite operation?

    <p>It can only substitute existing records that match the table's schema.</p> Signup and view all the answers

    What is a notable benefit of using the merge or upsert statement?

    <p>It allows multiple conditions to be enforced in one transaction.</p> Signup and view all the answers

    How can duplication be avoided when using the merge operation?

    <p>By providing only an insert in the 'when not matched' clause.</p> Signup and view all the answers

    What are the expectations for the Copy Into operation in Databricks?

    <p>The data schema should be consistent, and duplicates should be handled appropriately.</p> Signup and view all the answers

    Which operation is potentially cheaper for incrementally ingesting data according to the expected behaviors?

    <p>Copy Into operations.</p> Signup and view all the answers

    What is a key feature of the merge statement in terms of database operations?

    <p>It allows batch processing of multiple records.</p> Signup and view all the answers

    Which statement accurately reflects the limitations of Insert Overwrite?

    <p>It can only overwrite existing tables with records of the same schema.</p> Signup and view all the answers

    Which command can be used to check the metadata of an external table?

    <p>DESCRIBE EXTENDED external_table;</p> Signup and view all the answers

    What is a key characteristic of User-Defined Functions (UDFs) in Databricks?

    <p>UDFs introduce interprocess communication overhead in Python.</p> Signup and view all the answers

    To drop duplicates from a DataFrame, which Python command is correct?

    <p>dataFrame.dropDuplicates().count()</p> Signup and view all the answers

    Which statement correctly formats a date from a timestamp column in SQL?

    <p>SELECT date_format(datetime_col, 'MMM d, yyyy') AS date_col;</p> Signup and view all the answers

    When reading a CSV file into Databricks, which option can specify that the CSV has a header?

    <p>header='true'</p> Signup and view all the answers

    What aspect of Delta Lake ensures that all transactions are atomic?

    <p>ACID Transactions</p> Signup and view all the answers

    In Databricks, which of the following is NOT a method of dealing with null values?

    <p>delete from table_name where col is NULL;</p> Signup and view all the answers

    Which of the following SQL commands can create an external table with CSV data?

    <p>CREATE TABLE table_name USING CSV OPTIONS(header='true', delimiter='|') LOCATION 'path';</p> Signup and view all the answers

    In the context of Delta Lake, what does cloning refer to?

    <p>Taking a snapshot of the data at a specific point in time.</p> Signup and view all the answers

    When formatting date and time in Python using DataFrame, which command correctly formats the time column?

    <p>dataFrame.withColumn('time_col', date_format('datetime_col', 'HH:mm:ss'))</p> Signup and view all the answers

    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 and VERSION 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 and WHEN 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, and BINARY_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 using REFRESH TABLE.
    • External tables can be created using different methods (e.g., CSV, JDBC, JSON).

    Data Cleaning

    • COUNT_IF and COUNT with WHERE conditions can be used to identify null values.
    • DISTINCT can be used to remove duplicate records.
    • Date formatting can be achieved with date_format and regexp_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.

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