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.

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
No prior Spark or Databricks experience required

Outline

Introduction to Spark

Spark Runtime Architecture
Exploring Apache Spark Architecture in Databbricks
Introduction to Spark DataFrames and SQL
Reading and Writing Data with DataFrames
Distributed System Programming Fundamentals
Basic ETL with the DataFrame API
Flight Data ETL with the DataFrame API
Analyzing Transaction Data with DataFrames

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	May 07	09 AM - 01 PM (America/New_York)	English	$750.00
May 09	02 PM - 06 PM (Asia/Singapore)	English	$750.00
Jun 05	01 PM - 05 PM (Europe/London)	English	$750.00
Jun 16	01 PM - 05 PM (America/New_York)	English	$750.00
Jul 24	01 PM - 05 PM (Asia/Kolkata)	English	$750.00
Jul 24	09 AM - 01 PM (America/New_York)	English	$750.00

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Upcoming Public Classes

Apache Spark Developer

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.

Paid

Lab

Associate

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.