Build Data Pipelines with Delta Live Tables
In this course, you’ll learn how to define and schedule data pipelines that incrementally ingest and process data through multiple tables in the lakehouse using Delta Live Tables (DLT) in Spark SQL and Python. The course covers how to get started with DLT, how DLT tracks data dependencies in data pipelines, how to configure and run data pipelines using the Delta Live Tables UI, how to use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables in the lakehouse using Auto Loader and DLT, how to use APPLY CHANGES INTO syntax to process Change Data Capture feeds, and how to review event logs and data artifacts created by pipelines and troubleshoot DLT syntax.
- Beginner-level familiarity with basic cloud concepts (virtual machines, object storage, identity management)
- Ability to perform basic code development tasks (create compute, run code in notebooks, use basic notebook operations, import repos from git, etc)
- Intermediate familiarity with basic SQL concepts (CREATE, SELECT, INSERT, UPDATE, DELETE, WHILE, GROUP BY, JOIN, etc.)
Outline
- The Medallion Architecture
- Introduction to Delta Live Tables
- Using the Delta Live Tables UI
- Delta Live Tables Running Modes
- Pipeline Results
- Pipeline Event Logs
- Land New Data (Optional)
Upcoming Public Classes
Date | Time | Language | Price |
|---|---|---|---|
Oct 21 | 01 PM - 05 PM (Europe/London) | English | $750.00 |
Nov 04 | 09 AM - 01 PM (Asia/Kolkata) | English | $750.00 |
Nov 04 | 08 AM - 12 PM (America/New_York) | English | $750.00 |
Nov 07 | 08 AM - 12 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.
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