Lab Exercises on Spark DataFrames

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Questions and Answers

What is the primary role of Spark DataFrames in a data lakehouse?

  • To represent structured data in a distributed in-memory table format. (correct)
  • To serve as a method for real-time data storage.
  • To execute machine learning algorithms directly on raw data.
  • To manage network connections for distributed systems.

Which function can be used to display the first few rows of a DataFrame?

  • show() (correct)
  • list()
  • view()
  • display()

What is a necessary step before performing operations on a DataFrame?

  • Merge the DataFrame with another data source.
  • Apply machine learning models to the data.
  • Export the DataFrame to a CSV file.
  • Load the data into the DataFrame using Spark code. (correct)

Which command distinguishes filtering rows in a DataFrame?

<p>filter() (D)</p> Signup and view all the answers

In a DataFrame, what does the schema represent?

<p>The column names and data types. (B)</p> Signup and view all the answers

What comes after loading data as a Spark DataFrame in your notebook?

<p>Perform basic DataFrame operations such as filtering and sorting. (B)</p> Signup and view all the answers

Which statement about Spark DataFrames is NOT true?

<p>They are only useful for statistical analysis. (B)</p> Signup and view all the answers

What type of file can be loaded into a DataFrame as mentioned in the instruction?

<p>CSV file from Azure Data Lake Storage (ADLS). (C)</p> Signup and view all the answers

What is one way to handle missing values in a Spark DataFrame?

<p>Drop rows that contain any missing values. (A)</p> Signup and view all the answers

Which of the following is a benefit of using Spark DataFrame functions?

<p>To clean, manipulate, and transform data for further analysis. (B)</p> Signup and view all the answers

What is necessary to join two DataFrames in Spark?

<p>Common field to merge the DataFrames. (A)</p> Signup and view all the answers

Which function is used to combine two Spark DataFrames?

<p>join() (C)</p> Signup and view all the answers

When analyzing a joined DataFrame, what aspect can be evaluated?

<p>Customer buying behavior based on their purchase history. (D)</p> Signup and view all the answers

Which operation can be executed to convert data types in a DataFrame?

<p>DataFrame.withColumn() (A)</p> Signup and view all the answers

What is a key consideration when loading DataFrames from a data lakehouse?

<p>Use appropriate file formats and paths. (A)</p> Signup and view all the answers

Which operation is typically performed after joining two DataFrames?

<p>Filtering out irrelevant columns. (B)</p> Signup and view all the answers

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Study Notes

Lab Exercises on Apache Spark DataFrames in Data Lakehouses

  • Lab 1: Exploring DataFrames and Basic Operations

    • Understand Spark DataFrames as representations of structured data in a distributed in-memory table format.
    • Use Azure Databricks as the environment for running Spark code.
    • Create a new Databricks notebook for writing and executing Spark code.
    • Load CSV data from Azure Data Lake Storage (ADLS) into a Spark DataFrame, facilitating in-memory data manipulation.
    • Explore DataFrame structure using functions like show() or head() to view its contents and schema, including column names and data types.
    • Perform basic operations such as selecting columns, filtering rows based on conditions, and sorting data.

  • Lab 2: Data Cleaning and Transformation with DataFrames

    • Identify and address data issues in an existing DataFrame containing sales data, focusing on missing values and inconsistencies.
    • Utilize Spark functions to manage missing values, either by dropping affected rows or imputing with estimated data.
    • Apply data transformations, including filtering by product categories, changing data types, and creating new calculated columns.
    • Highlight the ability of Spark DataFrame functions to clean, manipulate, and transform data for analysis.

  • Lab 3: Joining DataFrames for Analysis

    • Load separate DataFrames for customer data and sales data from ADLS to enable combined analysis.
    • Identify common fields, such as customer ID, to facilitate the merging of DataFrames.
    • Use Spark DataFrame join functions, including join or inner, to combine customer and sales DataFrames.
    • Analyze the resultant joined DataFrame to derive insights into customer buying behavior by linking customer information with purchase history.

Key Functionalities

  • Spark DataFrames enable structured data representation and manipulation through distributed computing.
  • DataFrame functions provide powerful tools for data cleansing, transformation, and joining operations within a lakehouse architecture.

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