30 Questions
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
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
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
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
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
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
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
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
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
Match the operating systems with their corresponding command line interfaces:
Windows = Command Prompt MacOS = Terminal Linux = PowerShell Unix = Command Line Interface
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
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
Match the operations with their corresponding types:
INSERT = DML operation CREATE = DDL operation SELECT = DQL operation UPDATE = DML operation
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Explore the features of Spark Scala Shell, including running Delta Lake and writing data to it. Learn how to use Scala commands interactively. Practice with Spark and Scala!
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