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Home / / Getting Started with Lakeflow Spark Declarative Pipelines (ETL Pipelines)

Getting Started with Lakeflow Spark Declarative Pipelines (ETL Pipelines)

Learn what they are, set up your environment, create ETL pipelines, transform data with views, schedule runs, and use row tracking and incrementalization for efficient processing.

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  • Course Info
  • Instructor
  • What to know beforehand
  • System Requirements

Course Description

In Getting Started with Lakeflow Spark Declarative Pipelines, Zane Goodman introduces a modern way to build ETL workflows in Databricks using a more declarative, structured approach. You’ll learn what Lakeflow Spark Declarative Pipelines are, why they matter, and how they help simplify data engineering by letting you define what the pipeline should produce while the platform handles more of the orchestration behind the scenes. This course is designed to help you understand the core concepts before jumping into hands-on pipeline creation. You’ll set up the required environment, create an ETL pipeline, and work through common transformation patterns using views and structured data logic. Zane also covers scheduling so your pipelines can run automatically, along with row tracking and incrementalization to help process only what has changed instead of reprocessing everything from scratch. By the end, you’ll have a clear foundation for building Lakeflow Spark Declarative Pipelines that are cleaner, more maintainable, and better suited for scalable data workflows.

Course Outline ( Free Preview)

Module 00 - Introduction

Module 01 - What are Lakeflow Spark Declarative Pipelines?

Module 02 - Setup 11 min.

Module 03 - Creating an ETL Pipeline 14 min.

Module 04 - Views and Transforming Data 62 min.

Module 05 - Scheduling 6 min.

Module 06 - Row Tracking & Incrementalization 22 min.

This course includes:

  • 2+ hours of training
  • 7 Modules
  • Intermediate level content
  • Access on mobile and browser
  • Certificate of Completion
Free Community Plan View Pricing