Architecting Data Warehouses for Large-Scale Deployments

This course covers performance optimization, cost control, and security for large-scale data warehousing deployments.

This course is designed for Data Warehousing practitioners responsible for managing Databricks environments serving hundreds or thousands of users across multiple business units. You will gain the skills necessary to efficiently scale data warehousing operations while maintaining high performance, cost-effectiveness, and compliance with security standards.

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

Prerequisites

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

• SQL proficiency

• Data warehousing or Data platform architecture or management experience

• General cloud experience and understanding

Outline

Efficient Data Ingestion and Storage

• Data Architecture at Scale Introduction

• Data Warehouse Ingestion at Scale

• Demo - Ingestion and Transformation at Scale using Lakeflow

• Lab - Ingestion and Transformation using Lakeflow

• Lakehouse Federation and Foreign Catalogs

Multi-Workspace Strategy

• Databricks Accounts and Workspaces Overview

• Architecting for Multiple Workspaces

• Architecting Unity Catalog for Large Scale Environments

• Data Sharing in Large Scale Environments

Security and Governance at Scale

• Data Warehouse Enterprise Security

• Securing Data using Unity Catalog Lesson

• Demo - Implementing FGAC in Unity Catalog

• Lab - Applying Governance at Scale using ABAC

Identity and Administration

• Databricks Identities

• Deploying Databricks Solutions in the Enterprise

• Demo - Deploying Solutions using DABs and GitOps

• Auditing and Monitoring Databricks

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.

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Public and private courses taught by expert instructors across half-day to two-day courses

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

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 Lakeflow Jobs with SQL File tasks for production orchestration. The course follows a realistic retail dataset through the medallion architecture (Bronze → Silver → Gold).

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.

Apache Spark Developer

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Languages Available: English | 日本語 | 한국어

Build Data Pipelines with Lakeflow Spark Declarative Pipelines

This course introduces users to the essential concepts and skills needed to build data pipelines using Apache Spark™ Declarative Pipelines (SDP) in Databricks for incremental batch or streaming ingestion and processing through multiple streaming tables and materialized views. Designed for data engineers new to Spark Declarative Pipelines, the course provides a comprehensive overview of core components such as incremental data processing, streaming tables, materialized views, and temporary views, highlighting their specific purposes and differences.

Topics covered include:

• Developing and debugging ETL pipelines with the multi-file editor in Spark Declarative Pipelines using SQL (with Python code examples provided)

• How Spark Declarative Pipelines track data dependencies in a pipeline through the pipeline graph

• Configuring pipeline compute resources, data assets, trigger modes, and other advanced options

Next, the course introduces data quality expectations in Spark Declarative Pipelines, guiding users through the process of integrating expectations into pipelines to validate and enforce data integrity. Learners will then explore how to put a pipeline into production, including scheduling options, and enabling pipeline event logging to monitor pipeline performance and health.

Finally, the course covers how to implement Change Data Capture (CDC) using the AUTO CDC INTO syntax within Spark Declarative Pipelines to manage slowly changing dimensions (SCD Type 1 and Type 2), preparing users to integrate CDC into their own pipelines.