Section 2, ELT with Apache Spark
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Questions and Answers

Match the following types of joins with their descriptions:

Inner Join = Returns only the rows that have matching values in both DataFrames Left Join = Returns all rows from the left DataFrame and matched rows from the right Right Join = Returns all rows from the right DataFrame and matched rows from the left Full Outer Join = Returns all rows when there is a match in either DataFrame

Match the following DataFrame examples with their corresponding output for a left join:

df1 = {1: 'Alice', 2: 'Bob'} = Returns all rows from df1 with matched rows from df2 or NULL df2 = {1: 'Alice', 3: 'Charlie'} = Matched rows from df2 for existing keys in df1 df1 keys: {1, 2} = Keys exist in left DataFrame df1 df2 keys: {1, 3} = Keys exist in right DataFrame df2

Match the following DataFrame descriptions with the type of join they reference:

Returns NULL for unmatched rows on right = Left Join Includes all rows with possible NULLs = Full Outer Join Returns only matching keys from both DataFrames = Inner Join Returns NULL for unmatched rows on left = Right Join

Match the JSON parsing approach with its outcome:

<p>Easily parse JSON strings = Structured fields are created in DataFrame Use Apache Spark for data processes = Improved data processing capability Convert JSON fields = Parsed fields become individual columns Join DataFrames with matching keys = Data aggregation based on key relations</p> Signup and view all the answers

Match the following operations with their Spark DataFrame code examples:

<p>Inner Join = df1.join(df2, df1['key'] == df2['key'], 'inner') Left Join = df1.join(df2, df1['key'] == df2['key'], 'left') Right Join = df1.join(df2, df1['key'] == df2['key'], 'right') Full Outer Join = df1.join(df2, df1['key'] == df2['key'], 'full')</p> Signup and view all the answers

Match the following file-based data sources with their SQL syntax:

<p>CSV Files = SELECT * FROM csv.<code>/path/to/csv/files</code> Parquet Files = SELECT * FROM parquet.<code>/path/to/parquet/files</code> JSON Files = SELECT * FROM json.<code>/path/to/json/files</code> JDBC Data Sources = SELECT * FROM jdbc.<code>jdbc:postgresql://host:port/database_name?user=user&amp;password=password</code></p> Signup and view all the answers

Match the following types of views with their characteristics:

<p>Regular View = Named logical schema for complex queries Temporary View = Available only during the session CTE = Defined within a query and can be referenced within that query Database Table = Persisted in a catalog for future queries</p> Signup and view all the answers

Match the following Spark actions with their purposes:

<p>createOrReplaceTempView = Create a temporary view from a DataFrame spark.read.csv = Read data from a CSV file into a DataFrame spark.sql = Execute SQL queries against DataFrames or views inferSchema = Automatically determine data types of columns</p> Signup and view all the answers

Match the following data formats with their typical usage:

<p>CSV = Storing tabular data in plain text Parquet = Columnar storage for big data processing JSON = Data interchange format widely used in APIs Hive Tables = Data storage for large datasets in data lakes</p> Signup and view all the answers

Match the following Spark components with their typical actions:

<p>SparkSession = Entry point to interact with Spark DataFrame = Distributed collection of data organized into named columns SQLContext = Legacy component for running SQL queries RDD = Resilient distributed dataset, the basic abstraction in Spark</p> Signup and view all the answers

Match the following prefixes in SQL queries with their respective data types:

<p>csv = Comma-separated values files parquet = Columnar data storage format json = JavaScript Object Notation hive = Big data warehouse storage format</p> Signup and view all the answers

Match the following benefits of using array functions in Apache Spark with their descriptions:

<p>Handling Complex Data Structures = Efficiently work with nested arrays and hierarchical data Simplifying Data Manipulation = Easily perform operations like filtering and aggregating Performance Optimization = Leverage distributed processing for quick operations Improved Code Readability = Enhance maintainability with clear function usage</p> Signup and view all the answers

Match the following attributes of a view with their descriptions:

<p>Encapsulation = Hides complex SQL logic Reusability = Can be called multiple times in queries Persistence = Regular views store metadata in the catalog Session scoping = Temporary views do not persist after session ends</p> Signup and view all the answers

Match the following SQL statements with their intended actions:

<p>SELECT * FROM my_view = Query data from a created view SELECT * FROM my_temp_view = Query data from a temporary view CREATE VIEW my_view AS ... = Define a new view based on a query DROP TEMPORARY VIEW my_temp_view = Remove a temporary view from the session</p> Signup and view all the answers

Match the following operations that can be performed using array functions with their purposes:

<p>Array Concatenation = Combining multiple arrays into one Array Intersection = Finding common elements between arrays Array Explode = Converting an array column into multiple rows Array Distinct = Removing duplicate elements from an array</p> Signup and view all the answers

Match the following features of using Apache Spark for ETL processes with their advantages:

<p>Handling nested data = Facilitates clarity and accessibility Optimized performance = Quick processing on large datasets Code readability = Easier to maintain and understand Flexible data processing = Adaptable to various data types and structures</p> Signup and view all the answers

Match the following array functions with their functionalities:

<p>array_contains = Check if an element is present in an array array_join = Convert an array into a string explode = Flatten an array into multiple rows array_distinct = Return unique elements from an array</p> Signup and view all the answers

Match the following and their purposes in Apache Spark's ETL process:

<p>Extract = Retrieve data from various sources Transform = Modify data into a suitable format Load = Store data into target databases Analyze = Perform computations and insights on data</p> Signup and view all the answers

Match the following use cases of array functions with their benefits:

<p>Filtering arrays = Increases data manipulation efficiency Aggregating data = Simplifies complex calculations Transforming data = Directly operate on datasets Flattening arrays = Streamlines data structure</p> Signup and view all the answers

Match the following DataFrame operations with their descriptions:

<p>Create DataFrame = Initializing a structure to hold data Pivot = Transforming long format to wide format Show = Displaying the DataFrame contents GroupBy = Aggregating data based on a column</p> Signup and view all the answers

Match the following benefits of using the PIVOT clause with their explanations:

<p>Simplifies Data Analysis = Transforms data into a more readable format Improves Readability = Enhances clarity for reporting Efficient Aggregation = Allows quick generation of summaries Accessibility = Makes data easier to analyze</p> Signup and view all the answers

Match the programming concepts with their functions in Apache Spark:

<p>SQL UDF = Custom functions in SQL queries ELT = Extract, Load, Transform process DataFrame = Distributed collection of data Spark Session = Main entry point for DataFrame operations</p> Signup and view all the answers

Match the following DataFrame terms with their definitions:

<p>Long format = Data representation where each row is a record Wide format = Data representation with multiple columns for categories Revenue = Monetary income generated from products Quarter = A time period representing three months</p> Signup and view all the answers

Match the following functions with their output formats:

<p>groupBy = Aggregated DataFrame pivot = Wide formatted DataFrame sum = Total of values show = Console displayed output</p> Signup and view all the answers

Match the following terms with their associated operations:

<p>Create DataFrame = spark.createDataFrame() Display DataFrame = df.show() Aggregate Data = df.groupBy() Transform Data = df.pivot()</p> Signup and view all the answers

Match the following types of analysis benefits with their advantages:

<p>Comparative Analysis = Comparing different categories easily Visual Reporting = Enhances data visibility in reports Summarization = Quick insights from large data Effective Data Processing = Streamlines ELT workflows</p> Signup and view all the answers

Match each step in creating a UDF with its description:

<p>Initialize Spark Session = Create a Spark session to work with Define the UDF = Create a Python function for the desired operation Register the UDF = Make the UDF available for SQL queries Create or Load the DataFrame = Prepare data to be used with the UDF</p> Signup and view all the answers

Match each code snippet to its function:

<p>spark = SparkSession.builder.appName('UDF Example').getOrCreate() = Initialize Spark session multiply_by_two_udf = udf(multiply_by_two, IntegerType()) = Register the UDF result = spark.sql('SELECT Name, multiply_by_two(Number) AS Number_Doubled FROM people') = Use the UDF in a SQL query data = [('Alice', 1), ('Bob', 2), ('Charlie', 3)] = Create sample data for the DataFrame</p> Signup and view all the answers

Match the component with its role in the UDF process:

<p>Python function = Contains the logic for the UDF udf() function = Registers the function as a UDF createDataFrame() = Creates a DataFrame from data createOrReplaceTempView() = Makes the DataFrame available for SQL</p> Signup and view all the answers

Match each output function with its purpose:

<p>df.show() = Displays the contents of the DataFrame result.show() = Displays the result of the SQL query spark.udf.register() = Enables the UDF for SQL use udf() = Creates a UDF from a Python function</p> Signup and view all the answers

Match the following terms to their definitions:

<p>UDF = User-Defined Function for custom operations Spark SQL = Module providing SQL support in Spark DataFrame = Distributed collection of data organized into named columns SparkSession = Entry point to programming with Spark</p> Signup and view all the answers

Match the following components of SQL UDFs with their descriptions:

<p>Function Definition = Defines the operation to be performed UDF Registration = Registers the function within Spark Using UDF in SQL = Applies the function within SQL queries Benefits of SQL UDFs = Highlights advantages of using UDFs</p> Signup and view all the answers

Match the sources of functions in Apache Spark with their types:

<p>Built-in Functions = Provided under pyspark.sql.functions module User-Defined Functions (UDFs) = Custom functions registered within Spark Custom Functions = Defined directly in the script or application DataFrame Functions = Used for transformations on DataFrames</p> Signup and view all the answers

Match the benefits of SQL UDFs with their explanations:

<p>Custom Logic = Enables user-defined processing not available by default Reusability = Functions can be applied across different queries Flexibility = Enhances native Spark SQL capabilities Enhanced ELT Process = Applies transformation directly within SQL</p> Signup and view all the answers

Match the steps involved in using UDFs with their corresponding actions:

<p>Creating DataFrame = Building a sample DataFrame for SQL queries Defining UDF = Creating a custom function for specific operations Registering UDF = Making the function usable within Spark SQL Applying UDF = Using the function within a SQL context</p> Signup and view all the answers

Match the types of functions used in Spark with their features:

<p>Built-in Functions = Predefined functions for common tasks User-Defined Functions = Customizable based on user needs Custom Functions = Script-defined and flexible in use DataFrame API = Operations specifically for DataFrame manipulation</p> Signup and view all the answers

Match the logic of SQL UDFs with its characteristics:

<p>Custom Logic = Enables specific user-defined rules Reusability = Facilitates function use across multiple queries Flexibility = Allows enhanced data transformation Data Transformation = Directly manipulates data during queries</p> Signup and view all the answers

What method is used to remove duplicate rows in a DataFrame based on specified columns?

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

What format is used when saving the deduplicated DataFrame to a new table?

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

What is the purpose of the 'mode' parameter in the write operation?

<p>to define the overwrite behavior (B)</p> Signup and view all the answers

Which Spark function initializes a new Spark session?

<p>SparkSession.builder.appName (A)</p> Signup and view all the answers

What does the 'show()' method do when called on a DataFrame?

<p>Prints the contents of the DataFrame (B)</p> Signup and view all the answers

What is the primary reason for deduplicating data in an ETL process?

<p>To maintain data integrity (C)</p> Signup and view all the answers

Which line of code is responsible for creating a sample DataFrame?

<p>df = spark.createDataFrame(data, columns) (D)</p> Signup and view all the answers

What function can be combined with count to count rows based on a specific condition in PySpark SQL?

<p>when (B)</p> Signup and view all the answers

How can you count the number of rows where a column is NULL in Spark SQL?

<p>Using count combined with isNull (C)</p> Signup and view all the answers

In the provided example, what is the purpose of the statement count(when(df.Value.isNull(), 1))?

<p>To count rows where Value is NULL (B)</p> Signup and view all the answers

Which library must be imported to use PySpark SQL functions in the context described?

<p>pyspark.sql.functions (D)</p> Signup and view all the answers

In the expression count(when(df.Value == 10, 1)), what does '10' represent?

<p>The value to meet the condition (D)</p> Signup and view all the answers

What will the statement count_10.show() produce based on the given example?

<p>Count of rows where Value equals 10 (A)</p> Signup and view all the answers

What is required before creating a DataFrame in PySpark as illustrated?

<p>Initializing a Spark session (A)</p> Signup and view all the answers

Which method would you use to create a DataFrame in PySpark using sample data provided?

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

What is the first step in the process of extracting nested data in Spark?

<p>Initialize Spark Session (D)</p> Signup and view all the answers

In the given example, which method is used to rename columns in the DataFrame?

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

Which of the following is a valid way to extract nested fields in the DataFrame?

<p>df.select('Details.address.city') (C)</p> Signup and view all the answers

What will happen if the line 'df_extracted.show()' is executed?

<p>It will display the extracted DataFrame. (A)</p> Signup and view all the answers

How is the city extracted from the nested structure in the DataFrame?

<p>Using dot syntax (C)</p> Signup and view all the answers

What does the 'truncate=False' argument do when calling df.show()?

<p>It prevents truncation of long string values for better readability. (D)</p> Signup and view all the answers

What is the purpose of the from_json function in Spark?

<p>To parse JSON strings and create a struct column. (D)</p> Signup and view all the answers

Which command is used to display the resulting DataFrame after parsing the JSON?

<p>df_parsed.show() (D)</p> Signup and view all the answers

What is contained in the parsed_json column after using the from_json function?

<p>A flat representation of the parsed JSON fields. (D)</p> Signup and view all the answers

What is the significance of using truncate=False in the show() method?

<p>It ensures that long strings are shown completely without truncation. (A)</p> Signup and view all the answers

What kind of data is represented by the example DataFrame's 'json_string' column?

<p>Structured data in JSON format. (B)</p> Signup and view all the answers

Which Spark session method is used to create a new session in the example?

<p>SparkSession.builder() (C)</p> Signup and view all the answers

What is the purpose of the cast function in Spark DataFrames?

<p>To convert a data type of a column to another data type (A)</p> Signup and view all the answers

Which of the following correctly initializes a Spark session?

<p>SparkSession.builder().getOrCreate() (C)</p> Signup and view all the answers

What is the final structure of a DataFrame after casting a string date to a timestamp?

<p>It includes an additional column for the timestamp (C)</p> Signup and view all the answers

Which of the following would you expect after executing df.show()?

<p>A display of the DataFrame's contents in a tabular format (B)</p> Signup and view all the answers

Which data type is used when the 'StringDate' column is transformed into 'TimestampDate'?

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

Why is it important to cast string dates to timestamps in a DataFrame?

<p>Casting string dates enables time-based operations and queries. (B)</p> Signup and view all the answers

What will be the output of the DataFrame after casting if the StringDate was incorrectly formatted?

<p>The date will be set to null in the TimestampDate column (A)</p> Signup and view all the answers

What does the withColumn function accomplish in the DataFrame operations?

<p>It creates a new column or replaces an existing one with a specified transformation (D)</p> Signup and view all the answers

What is the primary purpose of creating a Common Table Expression (CTE)?

<p>To create temporary result sets that can be referenced in queries. (C)</p> Signup and view all the answers

In the context of Apache Spark, what is a temporary view used for?

<p>To allow applications to query data using SQL syntax without storing it permanently. (D)</p> Signup and view all the answers

How can you identify tables from external sources that are not Delta Lake tables?

<p>By filtering out tables that match the pattern '%.delta%'. (B)</p> Signup and view all the answers

What is the first step in using a Common Table Expression in a query?

<p>Define the CTE using a WITH clause. (D)</p> Signup and view all the answers

Which of the following steps is involved in registering a DataFrame for use in a CTE?

<p>Creating a temporary view from the DataFrame. (A)</p> Signup and view all the answers

What is an important consideration when listing tables in a database to identify Delta Lake tables?

<p>Filtering criteria must be applied to distinguish Delta Lake from non-Delta Lake tables. (A)</p> Signup and view all the answers

Which command is used to check the tables present in a specified database?

<p>SHOW TABLES IN database_name (C)</p> Signup and view all the answers

What does the command 'spark.sql(ct_query).show()' accomplish in the context of a CTE?

<p>It executes the CTE and displays the results in the console. (A)</p> Signup and view all the answers

The prefix 'csv' in a SQL query indicates that Spark should read from parquet files.

<p>False (B)</p> Signup and view all the answers

A temporary view remains available after the Spark session is closed.

<p>False (B)</p> Signup and view all the answers

You can query a view created from a JSON file using Spark SQL.

<p>True (A)</p> Signup and view all the answers

The SQL statement 'SELECT * FROM hive.database_name.table_name' accesses data from a Hive table.

<p>True (A)</p> Signup and view all the answers

The Spark session can be initialized using SparkSession.builder without any parameters.

<p>False (B)</p> Signup and view all the answers

Creating a view from a CSV file requires reading the file into a DataFrame first.

<p>True (A)</p> Signup and view all the answers

The command 'SELECT * FROM jdbc.jdbc:postgresql://...' is used to access CSV files directly.

<p>False (B)</p> Signup and view all the answers

You can create a view in Spark using the command df.createOrReplaceTempView('view_name').

<p>True (A)</p> Signup and view all the answers

The method used to remove duplicate rows in a DataFrame is called dropDuplicates.

<p>True (A)</p> Signup and view all the answers

Apache Spark can create a temporary view from a DataFrame derived from a JDBC connection.

<p>True (A)</p> Signup and view all the answers

The JDBC URL format for connecting to a PostgreSQL database is 'jdbc:mysql://host:port/database'.

<p>False (B)</p> Signup and view all the answers

In the deduplication process, duplicates are determined based on all columns by default.

<p>False (B)</p> Signup and view all the answers

To read data from a CSV file in Apache Spark, the 'spark.read.csv' method requires the 'header' parameter to be set to false.

<p>False (B)</p> Signup and view all the answers

The SparkSession must be initialized before any DataFrame operations can occur.

<p>True (A)</p> Signup and view all the answers

The DataFrame's dropDuplicates method retains all duplicate rows when executed.

<p>False (B)</p> Signup and view all the answers

Using PySpark, the DataFrame created from an external CSV file can also be used in ELT processes.

<p>True (A)</p> Signup and view all the answers

The 'createOrReplaceTempView' method is used to create a permanent view in Apache Spark.

<p>False (B)</p> Signup and view all the answers

To verify that a new Delta Lake table has deduplicated data, it is necessary to call the new_df.show() method.

<p>True (A)</p> Signup and view all the answers

The deduplication process can only be performed on DataFrames with at least three columns.

<p>False (B)</p> Signup and view all the answers

The show() method in Spark is used to display the content of the DataFrame in a console output format.

<p>True (A)</p> Signup and view all the answers

The JDBC driver for PostgreSQL must be specified in the Spark session configuration using the 'spark.jars' parameter.

<p>True (A)</p> Signup and view all the answers

A temporary view created in Spark cannot be queried using SQL syntax.

<p>False (B)</p> Signup and view all the answers

To create a DataFrame in Spark, you need to pass a list of data along with a schema that defines the column names.

<p>True (A)</p> Signup and view all the answers

The Spark session is initialized using the SparkSession.builder method.

<p>True (A)</p> Signup and view all the answers

The schema for the JSON string is defined using the StructType function, which allows for nested structures.

<p>True (A)</p> Signup and view all the answers

The data for creating the DataFrame consists of integers only.

<p>False (B)</p> Signup and view all the answers

The DataFrame is displayed using the df.show() method in Spark.

<p>True (A)</p> Signup and view all the answers

The resulting DataFrame includes separate columns for Year, Month, Day, Hour, Minute, and Second extracted from the Timestamp.

<p>True (A)</p> Signup and view all the answers

The regexp_extract function in Apache Spark is designed to convert timestamps into strings for easier manipulation.

<p>False (B)</p> Signup and view all the answers

A Spark session must be initialized before creating or loading a DataFrame.

<p>True (A)</p> Signup and view all the answers

The pyspark.sql.functions module does not support regular expressions for pattern extraction.

<p>False (B)</p> Signup and view all the answers

The Timestamp column should be cast to a string data type for accurate calendar data extraction.

<p>False (B)</p> Signup and view all the answers

The Spark DataFrame method can be used effectively in ETL processes to manipulate and extract data from sources.

<p>True (A)</p> Signup and view all the answers

The pivot method converts a DataFrame from wide format to long format.

<p>False (B)</p> Signup and view all the answers

Using the PIVOT clause can enhance the clarity and readability of data.

<p>True (A)</p> Signup and view all the answers

Aggregating data using the Pivot clause is less efficient compared to traditional methods.

<p>False (B)</p> Signup and view all the answers

A SQL UDF cannot be used to apply custom logic to data in Apache Spark.

<p>False (B)</p> Signup and view all the answers

Creating a DataFrame in Spark requires a SQL UDF.

<p>False (B)</p> Signup and view all the answers

The use of the pivot method does not alter the original DataFrame.

<p>True (A)</p> Signup and view all the answers

Flashcards

File-based Data Sources

Data stored in files like CSV, Parquet, or JSON.

CSV File

Comma-separated values file. A common data format.

Parquet File

Columnar file format, optimized for Spark's processing.

JSON File

JavaScript Object Notation file, data in key-value pairs.

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Hive Table

Data organized in a table within Hive. Used by Spark.

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View (SQL)

Named logical schema (a stored query).

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Temporary View

View only available during a session.

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CREATE VIEW

Spark SQL syntax to create a VIEW.

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Renaming Columns (DataFrame)

Changing column names in a DataFrame to more readable names (e.g., from a complicated nested field to "City" or "Zip")

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Nested Data Extraction (DataFrame)

Extracting data from nested columns in a DataFrame to create new, individual columns.

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Benefits of Array Functions (Spark ETL)

Array functions in Spark speed up and simplify ETL processes (extract, transform, load) involving array data structures

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Handling Complex Data Structures (Spark)

Array functions efficiently manage complex data arrangements like nested arrays, frequently used with JSON-based data.

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Simplified Data Manipulation (Array Functions)

Array functions make operations like filtering, transforming, aggregating or flattening data much easier without complex extra code.

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Performance Optimized Array Functions

Array functions leverage Spark's distributed processing for fast handling of large datasets.

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Data Transformation (Array Functions)

Array functions enable transformations such as array concatenation, intersection, and element extraction on data within arrays without extra complex functions.

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Improved Code Readability (Spark)

Array functions like explode, array_contains, improve clarity and maintainability of code by providing clear paths to handle array operations.

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Inner Join

Returns only rows with matching values in both DataFrames.

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Left Join

Returns all rows from the left DataFrame, and matching rows from the right. Non-matching right side values are NULL.

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Right Join

Returns all rows from the right DataFrame, and matching rows from the left. Non-matching left side values are NULL.

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Full Outer Join

Returns all rows from both DataFrames. Missing values are filled with NULL.

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Join Query Result

The result of joining two datasets (DataFrames) in Apache Spark based on the join type (inner, left, right, full outer).

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Spark DataFrame Pivot

A method to reshape a DataFrame from a long format to a wide format by aggregating data based on a specified column.

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Long Format DataFrame

A data structure with a single row for each data point, where values for different categories are represented in separate columns.

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Wide Format DataFrame

A data structure with one column for each category, in this case each quarter, and rows grouped by common identifiers.

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Spark SQL UDF

A user-defined function that can be used in Spark SQL queries for custom computation.

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ELT Process

A process that extracts, transforms, and loads data from various sources to a data warehouse or a data lake.

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Pivot Method

A Spark DataFrame method designed to transform data from long to wide format. Similar to a SQL pivot clause.

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Spark DataFrame

A data structure to represent data in tabular format within Apache Spark.

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groupBy (Spark)

A method that groups rows with the same values in specified columns for subsequent aggregation operations.

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Where are Spark functions defined?

Spark functions are defined in three main locations: built-in functions provided by Spark, user-defined functions (UDFs) created by the user, and custom functions written directly within your application code.

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What is a UDF?

A UDF is a custom function written by the user and registered within a Spark session. This allows the function to be used in Spark SQL queries or DataFrame operations.

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What does a UDF do?

UDFs provide a way to apply custom logic and transformations to data that are not natively supported by Spark SQL. This enhances the flexibility and power of Spark.

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Benefits of UDFs

UDFs provide several benefits: they allow you to apply custom logic, they can be reused across multiple queries and DataFrames, and they offer flexibility to enhance Spark SQL capabilities.

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How are built-in functions accessed?

Spark's built-in functions are accessed through the pyspark.sql.functions module in PySpark.

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Why use a UDF instead of a built-in function?

Use a UDF when you need to apply custom logic or transformations to data that are not natively supported by Spark SQL's built-in functions.

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How do you register a UDF?

To use a UDF in Spark, you must first register it using the spark.udf.register method.

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What are custom functions?

Custom functions are functions defined directly in your script or application. They are used for data manipulations within transformations or actions on DataFrames and RDDs.

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Spark Session

The entry point to Spark functionality. It manages resources and allows interacting with Spark's core services.

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UDF (User-Defined Function)

A custom function written in Python or Scala that can be used within Spark SQL queries.

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Register UDF

Making a UDF available for use in Spark SQL by associating it with a name.

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Create DataFrame

Constructing a Spark DataFrame from data. This can be done from various sources like files or lists.

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Temp View (DataFrame)

A temporary, named view of a DataFrame, allowing you to access data through SQL queries.

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Use UDF in SQL query

Calling the UDF within a Spark SQL query to apply its logic to the data.

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Spark SQL

Spark's SQL engine, allowing you to query and manipulate DataFrame data using SQL syntax.

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DataFrame

A distributed data structure in Spark, similar to a table in SQL, holding organized data.

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What is a Common Table Expression (CTE)?

A CTE is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It's defined within a query, making SQL code more organized and readable.

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How do you define a CTE?

You define a CTE within a SQL query using the WITH clause. It follows this pattern: WITH cte_name AS (SELECT ... FROM ...).

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Why use CTEs in Spark?

CTEs enhance the organization and readability of your ETL workflows by breaking down complex operations into smaller, more manageable units.

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How to identify non-Delta Lake tables?

To find tables that are not Delta Lake tables, you can use a query to list all tables in a Spark database and then filter out those that contain '.delta' in their name.

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What's the purpose of SHOW TABLES?

SHOW TABLES is a Spark SQL command used to list all existing tables within a specified database.

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What is a table format?

A table format defines how data is structured and stored within a table. Examples include CSV, Parquet, JSON, and Delta Lake.

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How to check a table's format?

You can check the format of a table to determine if it's a Delta Lake table. Look for the '.delta' extension in the table's name.

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Why is knowing table format important?

Knowing a table's format is crucial for choosing the appropriate data processing techniques and tools. For example, Delta Lake tables offer features like ACID properties and time travel.

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Count rows with condition

Count the number of rows in a Spark DataFrame that meet a specific condition.

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Count NULL values

Count the number of rows in a Spark DataFrame where a specific column has a NULL value.

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PySpark

The Python API for interacting with Apache Spark.

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when function

A PySpark SQL function that allows you to create conditional expressions within a DataFrame.

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count function

A PySpark SQL function used to count rows in a DataFrame, often combined with other functions to count specific conditions.

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isNull function

A PySpark function that checks if a column value is NULL.

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Deduplicate DataFrame

Remove duplicate rows from a Spark DataFrame based on specified columns.

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dropDuplicates() Method

The Spark DataFrame method used to eliminate duplicate rows.

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Delta Lake

An open-source storage layer for data lakes that provides ACID properties and time travel.

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Save Deduplicated DataFrame

Write the processed DataFrame to a new Delta Lake table or another format.

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Primary Key Validation

Ensuring that a primary key is unique across all rows in a Delta Lake table to maintain data integrity.

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Initialize Spark Session

Set up a Spark session for interacting with Spark functionalities.

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DataFrame Creation

Creating a Spark DataFrame to store and manipulate data in a table-like structure. Data can come from various sources, like files or lists.

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Casting a Column

Changing the data type of a column in a DataFrame. This is crucial for ensuring data is in the correct format for analysis and calculations.

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Timestamp

A data type representing a specific point in time. It allows for accurate time-based operations and analysis.

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withColumn

A Spark DataFrame function that lets you add or modify columns in a DataFrame. You specify the new column name and how to compute its values.

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cast

A PySpark function used to change the data type of a column in a DataFrame. This can convert strings to numbers, dates, or timestamps.

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TimestampDate

A new column created by casting a string date column to a timestamp data type.

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Nested Data Extraction

The process of pulling data from fields within a complex data structure like a nested JSON object or a column containing nested data.

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Dot Syntax

A way to access nested fields in Spark data using a period (.) to navigate layers within a structure.

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Sample DataFrame

A small, example DataFrame used to demonstrate a concept or technique. It doesn't contain real data.

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withColumnRenamed

A Spark DataFrame method used to change the name of a column.

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Show (DataFrame)

A method used to display the contents of a Spark DataFrame.

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Parse JSON into Structs

Transform JSON strings in a DataFrame into structured data using the from_json function and a predefined schema.

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Schema Definition for JSON

Creating a StructType that defines the structure of the JSON data, specifying field names, data types, and nesting.

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Flatten Nested Structs

Transforming a nested struct into separate columns for easier access to individual fields.

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SparkSession Initialization

Creating a SparkSession object, the entry point for interacting with Spark functionality.

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from_json Function

A Spark function that parses JSON strings into structured data based on a predefined schema.

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What is a StructType in Spark?

A Spark data type used to represent structured data like a JSON object, with fields and their corresponding data types.

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Purpose of 'col' function

A PySpark function used to access a column in a DataFrame by its name.

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What are file-based data sources?

Data sources that store information in files, like CSV, Parquet, or JSON. Spark reads data directly from these files.

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What are table-based data sources?

Data organized into tables like Hive tables or databases accessed through JDBC. Spark connects to these tables.

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What is a view?

A named query that simplifies accessing data and reusing complex operations.

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What is a temporary view?

A view that exists only during the current Spark session. Useful for temporary analysis.

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Why use views and CTEs?

To make your Spark code more readable and maintainable, by breaking down complex operations into smaller, reusable parts.

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How to access nested data?

Use dot syntax to access fields within nested data structures, like JSON objects, using a period (.) to navigate levels.

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What is from_json?

A Spark function to transform JSON strings in a DataFrame into structured data using a predefined schema.

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Create Spark Table from JDBC

Read data from a database using JDBC and create a temporary Spark table for further processing.

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Create Spark Table from CSV

Read data from a CSV file and create a temporary Spark table for further processing.

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Spark Temporary View

A named table in Spark that only exists during the current session, making it easy to work with data.

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CreateOrReplaceTempView

A Spark function to create or update a temporary view in Spark.

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Deduplication

Removing duplicate rows from a DataFrame based on specific columns.

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What are the steps to deduplicate a DataFrame?

  1. Initialize Spark Session. 2. Create or load the DataFrame. 3. Deduplicate based on specific columns. 4. Save the deduplicated DataFrame.
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How to create a sample DataFrame?

Use the spark.createDataFrame function to create a DataFrame from a list of data.

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Verify Deduplication

Confirm that the new Delta Lake table contains only unique rows.

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What is a Delta Lake table?

A table that uses the Delta Lake format for storage, offering features like ACID properties and time travel.

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Timestamp Column Casting

Converting a column containing timestamp strings to a proper timestamp data type.

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Extracting Calendar Data

Using functions to extract specific parts (year, month, hour, etc.) from a timestamp and adding them as new columns.

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regexp_extract Function

A Spark function that extracts specific patterns from a string column using regular expressions.

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Pattern Extraction

Identifying and extracting a specific pattern from a string column using the regexp_extract function.

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Data Transformation

Changing or extracting data from one format to another, often using Spark functions like regexp_extract or casting.

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ELT with Apache Spark

Extracting, Transforming, and Loading data between different systems using Spark functions.

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Schema

A blueprint that defines the structure of data. It tells Spark how to interpret each piece of information.

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JSON Schema

A specific schema designed to interpret structured JSON data. It defines the field names and datatypes within a JSON object.

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Parse JSON

The process of converting a JSON string into a structured data format that Spark can easily understand.

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Study Notes

ELT with Apache Spark

  • Extract data from a single file using spark.read. Follow appropriate format: CSV, JSON, Parquet.
  • Extract data from a directory of files using spark.read. Spark automatically reads all files in the directory.
  • Identify the prefix after the FROM keyword in Spark SQL to determine data type. Common prefixes include csv, parquet, json.
  • Create a view: a temporary display of data
  • Create a temporary view: a temporary display of data available only during the session
  • Create a CTE (Common Table Expression): temporary result sets for use in queries
  • Identify external source tables that are not Delta Lake tables. Check naming or format.
  • Create a table from a JDBC connection using spark.read.jdbc. Specify the URL, table, and properties for the connection.
  • Create a table from an external CSV file using spark.read.csv.
  • Deduplicate rows from an existing Delta Lake table by creating a new table from the existing table while removing duplicate rows. To use deduplication specify columns in .dropDuplicates().
  • Identify how the count_if function and count_where_x_is_null functions are used in Apache Spark to perform counts with conditional occurrences. Use count along with when and isNull function from PySpark SQL. The function count in Spark SQL inherently omits NULL values.
  • Validate a primary key by verifying all primary key values are unique.
  • Validate that a field is associated with just one unique value in another field using .groupBy() and .agg(countDistinct())
  • Validate that a value is not present in a specific field by using the filter() function or .count().
  • Cast a column to a timestamp using withColumn("TimestampDate",col("StringDate").cast("timestamp"))
  • Extract calendar data (year, month, day, hour, minute, second) from a timestamp column using year, month, dayofmonth, hour, minute, and second functions.
  • Extract a specific pattern from an existing string column using regexp_extract.
  • Extract nested data fields using the dot syntax. (e.g., Details.address.city)
  • Describe the benefits of using array functions (explode, flatten).
  • Describe the PIVOT clause as a way to convert data from a long format to a wide format.
  • Define a SQL UDF using a Python function and registering the UDF in Spark SQL.
  • Identify the location of a function(built-in, user-defined, and custom).
  • Describe the security model for sharing SQL UDFs.
  • Use CASE WHEN in SQL code to perform conditional logic in queries.

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Description

Test your knowledge on extracting, transforming, and loading data using Apache Spark. This quiz covers various data formats, creating views, and managing sources in Spark SQL. Prepare to evaluate your skills in handling data efficiently with Spark!

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