Chapter 1: Introduction to Data Retrieval with Scala

In this guide, you'll explore the process of retrieving matched rows from two DataFrames utilizing Scala within the Databricks environment.

Data integrity is a critical aspect that ensures the quality, consistency, and reliability of data throughout its lifecycle. Data engineering pipelines are vital structures that facilitate the collection, transformation, storage, and analysis of data from various sources.

Scala is a programming language that merges object-oriented and functional programming styles. Created by Martin Odersky, it was first released in 2003. The name "Scala" stands for "scalable language," highlighting its ability to evolve from simple scripts to complex systems.

Designed for productivity, expressiveness, and conciseness, Scala is versatile enough for a wide range of applications, from large-scale corporate solutions to scripting tasks. Its robust type system and expressive syntax have made it particularly popular in industries like banking.

If you aim to retrieve matched rows from two DataFrames based on multiple columns, you can utilize the join method from the Spark DataFrame API, ensuring you specify the relevant columns in your join condition.

💎 Import necessary Spark classes for DataFrame operations.

// Import libraries

import org.apache.spark.sql.{SparkSession, Row}

import org.apache.spark.sql.functions._

import org.apache.spark.sql.types._

💎 Create a SparkSession.

// Create Spark Session

val spark = SparkSession.builder().appName("RetrieveMatchedRows").getOrCreate()

💎 Create two sample DataFrames, df1 and df2, with shared columns, such as "EmpId," sourced from sample CSV files.

// File1 — Employee Info

val FileEmpInfo = "dbfs:/FileStore/EmployeeInfo.csv"

// File2 — Employee Distribution

val FileEmpDist = "dbfs:/FileStore/EmployeeDistribution-1.csv"

// Read data into dataframe 1 from File1

val df1 = spark.read.option("header", "true").csv(FileEmpInfo)

// Show the data from df1

df1.show()

💎 Read data into dataframe 2 from File2.

// Read data into dataframe 2 from File2

val df2 = spark.read.option("header", "true").csv(FileEmpDist)

// Show the data from df2

df2.show()

💎 Perform an inner join using the join method, specifying the "EmpId" column in the join condition and the join type as "inner."

// Join df1 and df2 on column EmpId with inner join

val joinDF = df1.join(df2, Seq("EmpId"), "inner")

💎 Finally, display the matched rows using the show() method on the joined DataFrame.

// Display the data

joinDF.show()

// Print schema of the data

joinDF.printSchema()

For a visual demonstration, check out the following video:

This video provides a detailed walkthrough on retrieving matched rows from two DataFrames using Scala in Databricks.

Chapter 2: Exploring Data Retrieval with PySpark

In addition to Scala, PySpark offers a robust way to achieve similar results.

To learn more about retrieving matched rows from DataFrames or files using PySpark, watch the following video:

This video explains how to retrieve matched rows from DataFrames or files by employing PySpark, making it an essential resource for your data engineering toolkit.

For further insights, feel free to explore our resources:

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