Databricks Asia-Pacific Learning Festival

This course teaches you the basics of using the Databricks Data Intelligence Platform for data engineering and warehousing. You’ll learn to navigate the workspace, manage objects like tables and clusters, ingest and transform data using Delta Lake and the medallion architecture, and orchestrate tasks with Databricks Workflows. The course also covers Databricks SQL, Delta Live Tables, and Unity Catalog, with an optional hands-on lab for practical experience.

Skill Level: Onboarding 
Duration: 3h 
Prerequisites: A basic understanding of data engineering principles and topics such as data collection, extraction, ingestion, and transformation.

This course provides a comprehensive overview of Databricks’ modern approach to data warehousing, highlighting how a data lakehouse architecture combines the strengths of traditional data warehouses with the flexibility and scalability of the cloud. You’ll learn about the AI-driven features that enhance data transformation and analysis on the Databricks Data Intelligence Platform. Designed for data warehousing practitioners, this course provides you with the foundational information needed to begin building and managing high-performant, AI-powered data warehouses on Databricks.

Skill Level: Onboarding 
Duration: 3h 
Prerequisites: The content was developed for participants with these skills/knowledge/abilities: 
A basic understanding of data warehousing principles and topics such as database administration, SQL, data manipulation, and storage.

In this course, you will develop the foundational skills needed to use the Databricks Data Intelligence Platform for executing basic machine learning workflows and supporting data science workloads. You will explore the platform from the perspective of a machine learning practitioner, covering topics such as feature engineering with Databricks Notebooks and model lifecycle tracking with MLflow. Additionally, you will learn about real-time model inference with Mosaic AI Model Serving and experience Databricks’ “glass box” approach to model development through AutoML. The course includes three instructor-led demonstrations, culminating in a comprehensive lab that reinforces the concepts covered in the demos.

Skill Level: Onboarding 
Duration: 3h 
Prerequisites: A beginner-level understanding of Python.
Basic understanding of DS/ML concepts (e.g. classification and regression models), common model metrics (e.g. F1-score), and Python libraries (e.g. scikit-learn and XGBoost).

This course provides a comprehensive introduction to Lakeflow Connect as a scalable and simplified solution for ingesting data into Databricks from a variety of data sources. You will begin by exploring the different types of connectors within Lakeflow Connect (Standard and Managed), learn about various ingestion techniques, including batch, incremental batch, and streaming, and then review the key benefits of Delta tables and the Medallion architecture.
From there, you will gain practical skills to efficiently ingest data from cloud object storage using Lakeflow Connect Standard Connectors with methods such as CREATE TABLE AS (CTAS), COPY INTO, and Auto Loader, along with the benefits and considerations of each approach. You will then learn how to append metadata columns to your bronze level tables during ingestion into the Databricks data intelligence platform. This is followed by working with the rescued data column, which handles records that don’t match the schema of your bronze table, including strategies for managing this rescued data.
The course also introduces techniques for ingesting and flattening semi-structured JSON data, as well as enterprise-grade data ingestion using Lakeflow Connect Managed Connectors.
Finally, learners will explore alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with foundational knowledge to support modern data engineering ingestion.

Skill Level: Associate 
Duration: 4h 
Prerequisites: 

• Basic understanding of the Databricks Data Intelligence platform, including Databricks Workspaces, Apache Spark, Delta Lake, the Medallion Architecture and Unity Catalog
• Experience working with various file formats (e.g., Parquet, CSV, JSON, TXT)
• Proficiency in SQL and Python
• Familiarity with running code in Databricks notebooks

This course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how to use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently using the industry-standard TCP-DI dataset as a reference. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks.

Skill Level: Associate 
Duration: 4h 
Prerequisites:

• Basic understanding of SQL and data querying concepts
• General knowledge of data warehousing concepts, including tables, schemas, and ETL/ELT processes is recommended
• Some experience with BI and/or data visualization tools is helpful but not required

This course focuses on the fundamentals of preparing data for machine learning using Databricks. Participants will learn essential skills for exploring, cleaning, and organizing data tailored for traditional machine learning applications. Key topics include data visualization, feature engineering, and optimal feature storage strategies. Through practical exercises, participants will gain hands-on experience in efficiently preparing data sets for machine learning within the Databricks. This course is designed for associate-level data scientists and machine learning practitioners. and individuals seeking to enhance their proficiency in data preparation, ensuring a solid foundation for successful machine learning model deployment.

Skill Level: Associate 
Duration: 4h 
Prerequisites:

• Familiarity with Databricks workspace and notebooks
• Familiarity with Delta Lake and Lakehouse
• Intermediate-level knowledge of Python