Working with DataFrame and Spark SQL | Data Engineer Full Course | Lecture 20

Welcome to the twentieth lecture of the Data Engineering Full Course series by AmpCode! 🚀 In this video, we’ll combine the power of DataFrames and Spark SQL to efficiently handle and analyze structured data. This session is designed to enhance your understanding of Spark's versatile data processing capabilities.

🔍 What You'll Learn:

How to integrate DataFrames with Spark SQL
Writing SQL queries on DataFrames
Creating temporary views for complex queries
Real-world examples of combining DataFrames and SQL for data analytics
By the end of this lecture, you’ll be proficient in using DataFrames and Spark SQL together to perform powerful data processing and analysis.

🔔 Don’t forget to subscribe to AmpCode for more lectures and updates. If you find this video useful, please like it and share it with others exploring data engineering. Let’s continue advancing our skills with Spark together!

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Installation links:

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Also check out our full Apache Hadoop course:

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Apache Spark Installation links:

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Audience

This tutorial has been prepared for professionals/students aspiring to learn deep knowledge of Big Data Analytics using Apache Spark and become a Spark Developer and Data Engineer roles. In addition, it would be useful for Analytics Professionals and ETL developers as well.

Prerequisites

Before proceeding with this full course, it is good to have prior exposure to Python programming, database concepts, and any of the Linux operating system flavors.

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Check out our full course topic wise playlist on some of the most popular technologies:

SQL Full Course Playlist-

PYTHON Full Course Playlist-

Data Warehouse Playlist-

Unix Shell Scripting Full Course Playlist-

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Channel Description-

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