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Building ETL Pipelines with SQL

This course teaches how to build production-ready ETL pipelines using pure SQL on the Databricks Data Intelligence Platform. Students learn Streaming Tables with Auto Loader for incremental ingestion, Materialized Views with incremental refresh for Silver-to-Gold transformations, AUTO CDC (FLOW AUTO CDC) for declarative SCD Type 1 and Type 2 dimension management, and Apache Spark Declarative Pipelines (SDP) to group these objects into production multi-table pipelines with data quality expectations, PRIVATE tables, and unified monitoring. The course follows a realistic retail dataset through the medallion architecture (Bronze → Silver → Gold). Orchestrating with Lakeflow Jobs is available as a bonus module.


Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

Skill Level
Associate
Duration
4h
Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:  

• Navigating the Databricks workspace (sidebar, Catalog Explorer, SQL Editor)

• Unity Catalog basics (catalogs, schemas, tables, volumes)

• Intermediate SQL (SELECT, JOIN, GROUP BY, CAST, COALESCE, CREATE TABLE)

• Data warehousing concepts (fact/dimension tables, star schemas, medallion architecture)

• Basic understanding of ETL workflows

Outline

1. SQL ETL on Databricks

SQL ETL on Databricks: The Big Picture

Demo: Exploring the Course Dataset and SQL Editor

Lab: Using the SQL Editor and Genie Code


2. Streaming Tables and Materialized Views

Building SQL ETL Pipelines

Demo: Building a Bronze-to-Silver Pipeline

Lab: Building a Customer Feedback Pipeline


3. Auto CDC

AUTO CDC Streaming Dimension Updates

Demo: Building Slowly Changing Dimensions with AUTO CDC

Lab: Building Slowly Changing Dimensions


4. Building Pipelines with Spark Declarative Pipelines

From Standalone SQL to Production Pipelines

Demo: Building an SDP Pipeline in the Multi-File Editor


5. (Bonus) Orchestrating with Lakeflow Jobs

Orchestrating SQL Pipelines with Lakeflow Jobs

Demo: Building a Lakeflow Job for the ETL Pipeline

Lab: Orchestrating SQL Pipelines with Lakeflow Jobs

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Oct 16
09 AM - 01 PM (Asia/Kolkata)
-
English
$750.00
Oct 16
01 PM - 05 PM (America/New_York)
-
English
$750.00
Nov 17
09 AM - 01 PM (America/Los_Angeles)
-
English
$750.00
Nov 24
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Dec 15
01 PM - 05 PM (Europe/Paris)
-
English
$750.00
Jan 26
09 AM - 01 PM (Asia/Singapore)
-
English
$750.00
Jan 26
01 PM - 05 PM (America/New_York)
-
English
$750.00

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Upcoming Public Classes

Databricks Performance Optimization - Mandarin Chinese

Databricks Performance Optimization 课程向数据工程师和分析师讲授如何在 Databricks Data Intelligence Platform 上诊断、衡量和修复性能瓶颈,以及如何将性能改进与成本关联起来。本课程遵循"先衡量、先利用平台"的工作流:让平台的自动优化功能承担繁重工作,通过 Query Profile 和系统表验证已应用的优化,再在必要时进行手动调优。

学员将以 Query Profile、Performance Insights 和查询历史记录系统表为基础,建立衡量性能的能力。在此基础上,他们将探索关键优化技术,包括数据布局与自调优托管表、liquid clustering、缓存与中间结果、shuffle、数据倾斜、溢出、行爆炸、驱动程序性能、Python UDF、serverless compute、Photon 以及成本归因。

在整个课程中,学员将针对合成零售数据中刻意设计的慢查询进行探索,并观察不同优化技术对性能的影响。他们将利用文件裁剪、任务执行时间、Photon 覆盖率和成本等依据来评估改进效果。两个基于场景的实验将强化从诊断到验证的完整优化工作流。

注意:Databricks Academy 正在将 Databricks 环境中的课堂教学转为基于 notebook 的形式,不再使用幻灯片进行授课。您可以在 Vocareum 实验环境中访问课程 notebook。

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

Paid
4h
Lab
instructor-led
Professional

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.