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.

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

- Developing Application with Spark or Prior Databricks Experience is required

Outline

Stream Processing and Analysis with Apache Spark

Introduction to Stream Processing
Spark Structured Streaming
Demo: Introduction to Spark Structured Streaming
Lab: Introduction to Spark Structured Streaming
Advanced Stream Processing and Analysis
Demo: Window Aggregation in Spark Structured Streaming
Lab: Window Aggregation in Spark Structured Streaming

Upcoming Public Classes

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

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

Private Class Request

If your company is interested in private training, please submit a request.

Request Private Training

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

Purchase now

Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

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.

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.