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Introduction to Delta Live Tables (DLT) in Databricks

6 Scenarios
2 Hours 35 Minutes
Intermediate
masterclass poster
Industry
general
Skills
approach
data-wrangling
batch-etl
data-quality
data-storage
data-understanding
Tools
databricks

Learning Objectives

Understand the difference between Procedural ETL and Declarative ETL approaches.
Understand DLT Datasets and their role in data pipelines.
Understand what expectations are in Delta Live Tables (DLT) and how they ensure data quality.
Understand how to create and configure a Delta Live Tables (DLT) pipeline using Python & SQL

Overview

This module introduces Delta Live Tables (DLT) in Databricks and its role in simplifying ETL workflows. You will start by understanding the limitations of procedural ETL, such as manual coding and debugging challenges, and explore the benefits of a declarative approach. The module covers Datasets and Views in DLT, explaining how Datasets define and store transformed data, while Views act as logical layers for intermediate transformations and quality checks. You will also learn about Data Quality Expectations, which enforce validation rules at every stage.

Finally, you will build and run DLT pipelines using Python and SQL, defining datasets, managing dependencies, scheduling workflows, and monitoring execution for efficient data processing.

Prerequisites

  • Familiarity with basic ETL concepts and processes.
  • Hands-on experience with PySpark for data transformations.
  • An understanding of basic data quality checks, such as null value handling and validation rules.
  • Have knowledge of Python functions or SQL syntax
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