Running Delta Lake in Spark Scala Shell

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Match the following with their description:

Databricks Community Edition = A type of Delta Lake table Spark Scala Shell = A cloud-based platform for running Delta Lake Delta Lake = A complete notebook environment with up-to-date runtime PySpark shell = An interactive Scala command-line interface

Match the following with their primary usage in the scenario:

Databricks = Running Delta Lake on a cloud platform Spark = Developing and running code samples for the book Scala = Evaluating interactive expressions in the shell Delta Lake = Storing and managing data

Match the following with their purpose in the context:

val data = spark.range(0, 10) = Creating a Delta table data.write.format("delta").mode("overwrite").save("/book/testShell" ) = Running interactive Scala commands Type :help for more information = Saving data to a file scala> = Accessing a complete notebook environment

Match the following with their functionality in the shell:

val data = spark.range(0, 10) = Saving data to a file data.write.format("delta").mode("overwrite").save("/book/testShell" ) = Creating a Delta table scala> = Accessing a complete notebook environment Type :help for more information = Running interactive Scala commands Signup and view all the answers

Match the following with their relationship to Delta Lake:

Databricks = A platform for running Delta Lake on the cloud Scala = A programming language used with Delta Lake Spark = A framework used to run Delta Lake Python = A language not used in this Delta Lake scenario Signup and view all the answers

Match the following with their role in the scenario:

Spark Scala Shell = An interactive Scala command-line interface Databricks Community Edition = A free cloud-based platform for running Delta Lake Delta Lake = A data storage and management system Python = Not used in this Delta Lake scenario Signup and view all the answers

Match the following code snippets with their functions:

import pyspark from delta import * = Import necessary libraries builder = pyspark.sql.SparkSession.builder.appName("MyApp") = Create a SparkSession builder spark = configure_spark_with_delta_pip(builder).getOrCreate() = Create a SparkSession object print(f"Hello, Spark version: {{spark.version}}") = Print Spark version Signup and view all the answers

Match the following code snippets with their functions:

df = spark.range(0, 10) = Create a DataFrame with a range of numbers df.write.format("delta") = Specify the file format for writing .mode("overwrite") = Specify the write mode .save("/book/chapter02/helloDeltaLake") = Specify the output file path Signup and view all the answers

Match the following code snippets with their purposes:

builder.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") = Load Delta Lake extensions builder.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") = Specify the catalog for Spark spark = configure_spark_with_delta_pip(builder).getOrCreate() = Create a SparkSession object with Delta Lake extensions print(f"Hello, Spark version: {{spark.version}}") = Verify the Spark version Signup and view all the answers

Match the following code snippets with their outputs:

print(f"Hello, Spark version: {{spark.version}}") = Print a message with the Spark version df.write.format("delta") = A Delta Lake file spark.range(0, 10) = A DataFrame with a range of numbers builder.appName("MyApp") = A SparkSession with a specified app name Signup and view all the answers

Match the following code snippets with their functions:

import pyspark = Import the PySpark module from delta import * = Import all modules from Delta Lake builder = pyspark.sql.SparkSession.builder.appName("MyApp") = Create a SparkSession builder with a specified app name df = spark.range(0, 10) = Create a DataFrame with a range of numbers Signup and view all the answers

Match the following code snippets with their effects:

df.write.format("delta") = Specify the file format for writing as Delta Lake .mode("overwrite") = Overwrite the output file if it exists .save("/book/chapter02/helloDeltaLake") = Save the DataFrame to a file spark = configure_spark_with_delta_pip(builder).getOrCreate() = Create a SparkSession object with Delta Lake extensions Signup and view all the answers

Match the operating systems with their corresponding command line interfaces:

Windows = Command Prompt MacOS = Terminal Linux = PowerShell Unix = Command Line Interface Signup and view all the answers

Match the programming concepts with their descriptions:

PySpark = Python API for Apache Spark Delta Lake = Open-table format for data storage Parquet = File format for storing data RDBMS = Relational Database Management System Signup and view all the answers

Match the file formats with their descriptions:

Parquet = Column-oriented file format JSON = JavaScript Object Notation CSV = Comma Separated Values Delta Lake = Open-table format for data storage Signup and view all the answers

Match the operations with their corresponding types:

INSERT = DML operation CREATE = DDL operation SELECT = DQL operation UPDATE = DML operation Signup and view all the answers

Match the technologies with their descriptions:

Spark = Unified analytics engine Delta Lake = Open-table format for data storage PySpark = Python API for Apache Spark Parquet = Column-oriented file format Signup and view all the answers

Match the database systems with their characteristics:

RDBMS = Uses SQL for querying Delta Lake = Supports ACID transactions Spark = In-memory data processing Parquet = Column-oriented storage Signup and view all the answers

Match the following Parquet file features with their benefits:

Column metadata = Enables better query performance Compression and encoding = Reduces storage costs Row groups = Improves data processing efficiency Data schemas = Provides data organization Signup and view all the answers

Match the following Parquet file characteristics with their advantages:

Compression = Reduces storage space Interoperability = Enables compatibility with various tools Metadata = Improves query performance Encoding = Enhances data processing speed Signup and view all the answers

Match the following Parquet file features with their effects on data:

Min/max values = Enables data skipping Data schemas = Organizes data efficiently Column metadata = Provides data insights Row groups = Manages data rows efficiently Signup and view all the answers

Match the following Parquet file benefits with their implications:

Cost-effectiveness = Reduces storage costs Improved performance = Enhances query efficiency Interoperability = Increases tool compatibility Data organization = Simplifies data management Signup and view all the answers

Match the following Parquet file aspects with their significance:

Compression and encoding = Reduces data size Metadata = Improves query speed Row groups = Manages data rows Data schemas = Organizes data structure Signup and view all the answers

Match the following Parquet file features with their relevance:

Column metadata = Provides data insights Interoperability = Enhances tool compatibility Compression and encoding = Reduces storage needs Data schemas = Organizes data efficiently Signup and view all the answers

Match the following code components with their purposes in the Delta Lake scenario:

DATALAKE_PATH = Specifies the location for storing delta lake files columns = Defines the structure of the patient data spark.createDataFrame = Creates a DataFrame from the patient data mode('append') = Overwrites existing data in the delta lake Signup and view all the answers

Match the following code components with their effects on the delta lake:

df.write.format('delta') = Converts the DataFrame to a delta lake file df.coalesce(1) = Repartitions the DataFrame into a single partition save(DATALAKE_PATH) = Writes the DataFrame to the specified location write.format('delta').mode('append') = Appends new data to the existing delta lake file Signup and view all the answers

Match the following code components with their relationships to the patient data:

patientID = Represents the unique identifier for each patient f'Patient {patientID}' = Generates a patient name string based on the patient ID 'Phoenix' = Specifies the default location for each patient t = (patientID, f'Patient {patientID}', 'Phoenix') = Creates a tuple representing a patient's data Signup and view all the answers

Match the following code components with their roles in the transaction log:

_checkpoint = Creates a checkpoint file for the transaction log port-00000.parquet = Represents the first part of the delta lake file OOOOO.json = Contains metadata for the delta lake file delta log = Stores the history of all transactions in the delta lake Signup and view all the answers

Match the following code components with their effects on the files:

Action Add part-OOOO1 = Adds a new part file to the delta lake OOOO1.json = Creates a new JSON file for the delta lake metadata port-00009.parquet = Writes the last part of the delta lake file OOOO9.json = Updates the metadata for the last part of the delta lake file Signup and view all the answers

Match the following code components with their purposes in the data pipeline:

df = spark.createDataFrame( [t], columns) = Converts the patient data into a DataFrame df.write.format('delta').mode('append').save(DATALAKE_PATH) = Writes the DataFrame to the delta lake for index in range(9): = Loops through the data to create multiple commits patientID = 10 + index = Generates a unique patient ID for each iteration Signup and view all the answers

Running Delta Lake in Spark Scala Shell

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Questions and Answers

Match the following with their description:

Match the following with their primary usage in the scenario:

Match the following with their purpose in the context:

Match the following with their functionality in the shell:

Match the following with their relationship to Delta Lake:

Match the following with their role in the scenario:

Match the following code snippets with their functions:

Match the following code snippets with their functions:

Match the following code snippets with their purposes:

Match the following code snippets with their outputs:

Match the following code snippets with their functions:

Match the following code snippets with their effects:

Match the operating systems with their corresponding command line interfaces:

Match the programming concepts with their descriptions:

Match the file formats with their descriptions:

Match the operations with their corresponding types:

Match the technologies with their descriptions:

Match the database systems with their characteristics:

Match the following Parquet file features with their benefits:

Match the following Parquet file characteristics with their advantages:

Match the following Parquet file features with their effects on data:

Match the following Parquet file benefits with their implications:

Match the following Parquet file aspects with their significance:

Match the following Parquet file features with their relevance:

Match the following code components with their purposes in the Delta Lake scenario:

Match the following code components with their effects on the delta lake:

Match the following code components with their relationships to the patient data:

Match the following code components with their roles in the transaction log:

Match the following code components with their effects on the files:

Match the following code components with their purposes in the data pipeline:

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