Databricks Streaming and Delta Live Tables

This course provides a comprehensive understanding of Spark Structured Streaming and Delta Lake, including computation models, configuration for streaming read, and maintaining data quality in a streaming environment.

Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.

Languages Available: English | 日本語 | Português BR | 한국어

Skill Level

Professional

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

Ability to perform basic code development tasks using the Databricks Data Engineering and Data Science workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc.)
Intermediate programming experience with PySpark
Extract data from a variety of file formats and data sources
Apply a number of common transformations to clean data
Reshape and manipulate complex data using advanced built-in functions
Intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions, etc.)
Beginner experience configuring and scheduling data pipelines using the Delta Live Tables (DLT) UI
Beginner experience defining Delta Live Tables pipelines using PySpark
Ingest and process data using Auto Loader and PySpark syntax
Process Change Data Capture feeds with APPLY CHANGES INTO syntax
Review pipeline event logs and results to troubleshoot DLT syntax

Outline

Introduction to Streaming

* Streaming Data Concepts

* Introduction to Structured Streaming

* Reading from a Streaming Query

* Streaming from Delta Lake

* Streaming Query Lab

Aggregations, Time Windows, Watermarks

* Aggregations, Time Windows, Watermarks

* Event Time + Aggregatios over Time Windows

* Stream Aggregation Lab

* Windowed Aggregation with Watermark

Streaming ETL Patterns with DLT

* Data Ingestion Patterns

* Auto Load to Bronze

* Stream from Multiplex Bronze

* Quality Enforcement Patterns

* Quality Enforcement

* Streaming ETL Lab

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Jun 02	09 AM - 01 PM (Europe/London)	English	$750.00
Jun 03	09 AM - 01 PM (Asia/Kolkata)	English	$750.00
Jun 06	09 AM - 01 PM (America/New_York)	English	$750.00
Aug 04	09 AM - 01 PM (Europe/London)	English	$750.00
Aug 06	01 PM - 05 PM (America/Los_Angeles)	English	$750.00
Aug 07	09 AM - 01 PM (Asia/Kolkata)	English	$750.00
Sep 02	01 PM - 05 PM (Europe/London)	English	$750.00
Sep 04	09 AM - 01 PM (America/New_York)	English	$750.00
Sep 05	01 PM - 05 PM (Australia/Sydney)	English	$750.00
Oct 02	09 AM - 01 PM (Europe/London)	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

Data Analyst

Data Warehousing with Databricks

This course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently using the industry-standard TCP-DI dataset as a reference. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks.

Paid

Lab

Associate

Apache Spark Developer

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

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.