Data Ingestion with Delta Lake

This course prepares data professionals to leverage the Databricks Intelligence Platform to productionalize ETL pipelines. Students will use Delta Live Tables with Spark SQL and Python to define and schedule pipelines that incrementally process new data from a variety of data sources into the Lakehouse. Students will also orchestrate tasks with Databricks Workflows and promote code with Databricks Repos.

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

Skill Level

Associate

Duration

Prerequisites

Beginner 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

Set Up and Load Delta Tables
Basic Transformations
Load Data Lab
Cleaning Data
Complex Transformations
SQL UDFs
Advanced Delta Lake Features
Manipulate Delta Tables Lab

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Jan 27	01 PM - 05 PM (America/New_York)	English	$750.00
Jan 29	09 AM - 01 PM (Asia/Singapore)	English	$750.00
Jan 31	08 AM - 12 PM (Europe/London)	English	$750.00
Feb 25	09 AM - 01 PM (America/Los_Angeles)	English	$750.00
Feb 26	01 PM - 05 PM (Europe/London)	English	$750.00
Feb 27	09 AM - 01 PM (Australia/Sydney)	English	$750.00
Mar 25	09 AM - 01 PM (Europe/London)	English	$750.00
Mar 26	01 PM - 05 PM (Asia/Kolkata)	English	$750.00
Mar 26	09 AM - 01 PM (America/New_York)	English	$750.00
Apr 22	01 PM - 05 PM (Europe/London)	English	$750.00
Apr 24	09 AM - 01 PM (Asia/Kolkata)	English	$750.00
Apr 24	01 PM - 05 PM (America/New_York)	English	$750.00

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Upcoming Public Classes

Data Engineer

Databricks Data Privacy

This content provides a comprehensive guide to managing data privacy within Databricks. It covers key topics like Delta Lake architecture, regional data isolation, GDPR/CCPA compliance, and Change Data Feed (CDF) usage. Through practical demos and hands-on labs, participants learn to use Unity Catalog features for securing sensitive data and ensuring compliance, empowering them to safeguard data integrity effectively.

Paid

Lab

Introductory

Generative AI Engineer

Generative AI Application Deployment and Monitoring

Ready to learn how to deploy, operationalize, and monitor generative AI applications? This content will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

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

Machine Learning Practitioner

Machine Learning at Scale

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.