Developing Applications with Apache Spark™

Master scalable data processing with Apache Spark in this hands-on course. Learn to build efficient ETL pipelines, perform advanced analytics, and optimize distributed data transformations using Spark’s DataFrame API. Explore grouping, aggregation, joins, set operations, and window functions. Work with complex data types like arrays, maps, and structs while applying best practices for performance optimization.

Skill Level

Associate

Duration

Prerequisites

- Basic programming knowledge

- Familiarity with Python

- Basic understanding of SQL queries (SELECT, JOIN, GROUP BY)

- Familiarity with data processing concepts

- "Introduction to Apache Spark Course" or Previous Databricks experience required

Outline

Developing Applications with Spark

DataFrame API Basics
Demo: (Optional) Basic ETL with the DataFrame API
Grouping and Aggregating Data
Demo: Grouping and Aggregating Data
Lab: Grouping and Aggregating E-Commerce Data
Relational Operations
Demo: Data Relational Operations in Apache Spark
Working with Complex Data
Demo: Working with Complex Data Types in Apache Spark
Lab: Working with Complex Data Types in E-Commerce Data

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	May 14	09 AM - 01 PM (America/New_York)	English	$750.00
Jun 16	01 PM - 05 PM (Asia/Kolkata)	English	$750.00

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Upcoming Public Classes

Apache Spark Developer

Stream Processing and Analysis with Apache Spark™

Learn the essentials of stream processing and analysis with Apache Spark in this course. Gain a solid understanding of stream processing fundamentals and develop applications using the Spark Structured Streaming API. Explore advanced techniques such as stream aggregation and window analysis to process real-time data efficiently. This course equips you with the skills to create scalable and fault-tolerant streaming applications for dynamic data environments.

SQL Analytics on Databricks

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights.

You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL.

Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

Apache Spark Developer

Introduction to Apache Spark™

This course offers essential knowledge of Apache Spark™, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows.