Chapter 3 [Hard: Matching]: Delta Tables Creation Methods Spark Delta Tables

Managed tables = Spark manages both the metadata and the actual data Unmanaged tables = User specifies the exact location where to save the underlying data DataFrameWriter API = Used to create a table and insert data into it simultaneously Delta table creation = Requires specifying the location where to save the underlying data

df_rate_codes.write.format('delta').saveAsTable = Saves a DataFrame as a managed Hive table df_rate_codes = spark.read.format('csv').load(INPUT_PATH) = Populates a DataFrame from a CSV file spark.read.format('csv').option('inferSchema', True).option('header', True).load(INPUT_PATH) = Specifies options for reading a CSV file df_rate_codes.write.format('delta') = Creates a Delta table without specifying a location

Managed tables = /spark-warehouse subfolder or /user/hive/warehouse folder Unmanaged tables = User-specified location Delta tables = /databricks-datasets/nyctaxi/taxizone/ DataFrames = No default storage location

<p>Simultaneously creating a table and inserting data into it = DataFrameWriter API Specifying the exact location where to save the underlying data = Unmanaged tables Metadata management = Managed tables Creating a Delta table without specifying a location = Delta table creation</p> Signup and view all the answers

<p>DELTA_LAKE_PATH = 'dbfs:/mnt/datalake/book/chapter03/createDeltaTableWithDataFrameWriter' = Specifies the location for saving a Delta table df_rate_codes.write.format('delta').saveAsTable('taxidb.rateCard') = Saves a DataFrame as a managed Hive table df_rate_codes = spark.read.format('csv').option('inferSchema', True).option('header', True).load(INPUT_PATH) = Populates a DataFrame from a CSV file INPUT_PATH = '/databricks-datasets/nyctaxi/taxizone/taxi_rate_code.csv' = Specifies the input path for a CSV file</p> Signup and view all the answers

<p>df.schema = Display the schema of a DataFrame df_for_append.display() = Display the partial output of a DataFrame df = spark.read.format('delta').table('taxidb.YellowTaxis') = Load a DataFrame from a Delta table df = spark.read.option('header', 'true').schema(yellowTaxiSchema).csv('/mnt/datalake/book/data files/YellowTaxis_append.csv') = Load a DataFrame from a CSV file with a specified schema</p> Signup and view all the answers

<p>df.write.format('delta').save('taxidb.YellowTaxis') = Create a new Delta table spark.read.format('delta').table('taxidb.YellowTaxis') = Load a DataFrame from a Delta table df_for_append.write.format('delta').mode('append').save('taxidb.YellowTaxis') = Append a DataFrame to a Delta table dfschema = df.schema = Extract the schema of a DataFrame</p> Signup and view all the answers

<p>Managed Tables = Delta tables created without a location DataFrameWriter API = Used to create and insert data into Delta tables Unmanaged Tables = Delta tables created with a specific location Partitioning = Dividing data based on specific criteria</p> Signup and view all the answers

<p>header = Specify whether the CSV file has a header row schema = Specify the schema of a DataFrame format = Specify the file format of a DataFrame mode = Specify the save mode of a DataFrame</p> Signup and view all the answers

<p>SQL = Used by SQL developers to create Delta tables DataFrameWriter API = Used by Python users to create Delta tables DeltaTableBuilder API = Used to create Delta tables with fine-grained control PySpark DataFrameReader API = Used to read Delta tables</p> Signup and view all the answers

<p>Delta table = A managed table that stores data in a columnar format CSV file = A file that stores data in a comma-separated format DataFrame = A temporary result set that stores data in memory DataFrameWriter = An interface for writing a DataFrame to a data source</p> Signup and view all the answers

<p>Created with a location = Unmanaged Table Created without a location = Managed Table Partitions data = Partitioned Table Can be read using ANSI SQL = Standard Delta Table</p> Signup and view all the answers

<p>CREATE TABLE = Used to create a Delta table using SQL DataFrameWriter API = Used to create and insert data into a Delta table INSERT statement = Used to write to a Delta table PySpark DataFrameReader API = Used to read a Delta table</p> Signup and view all the answers

<p>Generated Columns = Columns with automatically generated values based on a user-specified function Partitioned Tables = Tables that divide data based on specific criteria Managed Tables = Delta tables that can be read using standard ANSI SQL PySpark DataFrame API = Used to append a DataFrame to a Delta table</p> Signup and view all the answers

<p>Creating a Delta table with a location = Unmanaged table Creating a Delta table without a location = Managed table Using the DataFrameWriter API = Unmanaged table Using SQL DDL = Managed table</p> Signup and view all the answers

<p>DataFrameWriter API = A collection of functions to read, write, and manipulate DataFrames PySpark DataFrames = Resembling relational database tables or Excel spreadsheets with headers Delta tables = Tables created with the DataFrameWriter API or SQL DDL Managed tables = Delta tables created without a location</p> Signup and view all the answers

<p>Managed table = A Delta table created without a location Unmanaged table = A Delta table created with a location External table = A table that resides in a external storage Delta table = A table created with the DataFrameWriter API or SQL DDL</p> Signup and view all the answers

<p>DataFrameWriter API = A collection of functions to read, write, and manipulate DataFrames Creating a managed table = A Delta table created without a location Creating an unmanaged table = A Delta table created with a location Reading a Delta table = Using the DataFrameWriter API to read a Delta table</p> Signup and view all the answers

<p>DataFrame = A collection of data organized into rows and columns DataFrameWriter API = A set of functions to manipulate DataFrames PySpark = A Python library for big data processing Delta table = A table created with the DataFrameWriter API or SQL DDL</p> Signup and view all the answers

<p>Using the DataFrameWriter API = Creates a managed Delta table With Z-ordering = Reduces latency for queries with predicates that include the partition columns With liquid clustering = Automates the process of aligning data to query patterns Specifying a PARTITIONED BY clause = recommended approach to align data to your query patterns to increase query perfromance.</p> Signup and view all the answers

<p>Partitioning = Improves query performance for predicates that include the partition columns Z-ordering = Reduces latency for queries with range predicates Liquid clustering = Automates the process of aligning data to query patterns DataFrameWriter API = Creates a managed Delta table</p> Signup and view all the answers

<p>Partition columns = Columns used to divide data into smaller groups Predicates = Conditions used in WHERE clauses to filter data Query patterns = Frequent query patterns that benefit from partitioning Managed tables = Tables managed by PySpark for data consistency</p> Signup and view all the answers

<p>Partitioning = Improves query performance for predicates that include the partition columns Z-ordering = Reduces latency for queries with range predicates Liquid clustering = Automates the process of aligning data to query patterns Managed tables = Provides data consistency and automatic partitioning</p> Signup and view all the answers

<p>CREATE TABLE... USING DELTA = Supports ANSI SQL standards Catalog registration = Registers a table with a database.table_name notation DataFrameWriter API = Provides a programmatic way to create Delta tables Delta table creation with SQL DDL = Allows for fine-grained control over table attributes</p> Signup and view all the answers

<p>write.format('delta').save() = Saves a DataFrame to a Delta table CREATE TABLE... USING DELTA = Creates a Delta table using SQL DDL catalog.registerTable() = Registers a table with a catalog df.write.partitionBy() = Partitions a DataFrame by a specified column</p> Signup and view all the answers

<p>SQL DDL command = Supports ANSI SQL standards DataFrameWriter API = Provides a programmatic way to create Delta tables Catalog registration = Allows for easy table management Delta table creation method = Gives fine-grained control over table attributes</p> Signup and view all the answers

<p>df.write.format('delta').save() = Creates a Delta table from a DataFrame CREATE TABLE... USING DELTA = Creates a Delta table using SQL DDL catalog.registerTable() = Registers a table with a catalog df.write.partitionBy() = Partitions a DataFrame by a specified column</p> Signup and view all the answers

<p>Fine-grained control = Allows for precise control over table attributes ANSI SQL standards = Supports standard SQL syntax Programmatic creation = Supports creation using the DataFrameWriter API Catalog registration = Registers a table with a database.table_name notation</p> Signup and view all the answers

<p>df.write.format('delta').save() = Creates a Delta table CREATE TABLE... USING DELTA = Creates a Delta table catalog.registerTable() = Registers a table with a catalog df.write.partitionBy() = Partitions a DataFrame</p> Signup and view all the answers

<p>Fine-grained control = Needed for precise control over table attributes ANSI SQL standards = Used for creating Delta tables using SQL DDL Programmatic creation = Used for creating Delta tables using the DataFrameWriter API Catalog registration = Used for registering tables with a catalog</p> Signup and view all the answers

<p>Reading a Delta table = Using SQL query Creating a Delta table = Using the DataFrameWriter API Managing a Delta table = Using PySpark DataFrames Deleting a Delta table = Using a SQL DROP TABLE statement</p> Signup and view all the answers

<p>df.write.format('delta').save('/mnt/datalake/table') = Creating a managed Delta table spark.sql('CREATE TABLE default.people USING DELTA LOCATION 'mnt/datalake/table'') = Creating an unmanaged Delta table df.write.format('delta').save('/mnt/datalake/table').option('overwrite', 'true') = Overwriting a Delta table spark.sql('DESCRIBE FORMATTED default.people') = Describing a Delta table</p> Signup and view all the answers

<p>Managed table = A table that is fully managed by Databricks Unmanaged table = A table that is not fully managed by Databricks External table = A table that is stored in a location outside of Databricks Delta table = A table that stores data in a columnar format</p> Signup and view all the answers

<p>spark.read.format('delta').load('/mnt/datalake/table') = Reading a Delta table spark.read.format('parquet').load('/mnt/datalake/table') = Reading a Parquet file spark.read.format('csv').load('/mnt/datalake/table') = Reading a CSV file spark.read.format('json').load('/mnt/datalake/table') = Reading a JSON file</p> Signup and view all the answers

<p>df.write.format('delta').save('/mnt/datalake/table') = Saving a DataFrame as a Delta table df.write.format('parquet').save('/mnt/datalake/table') = Saving a DataFrame as a Parquet file df.write.format('csv').save('/mnt/datalake/table') = Saving a DataFrame as a CSV file df.write.format('json').save('/mnt/datalake/table') = Saving a DataFrame as a JSON file</p> Signup and view all the answers