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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Private Class Request

If your company is interested in private training, please submit a request.

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Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

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

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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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.

Databricks One for Business Users

This brief 30-minute training introduces the audience to the features and capabilities of Databricks One.

Machine Learning Practitioner

Advanced Machine Learning with Databricks

This course is aimed at data scientists and machine learning practitioners and consists of two, four-hours modules.

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.

Advanced Machine Learning Operations

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.