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: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

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

Self-Paced

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

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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

Data Warehousing Practitioner

Data Interoperability with Unity Catalog

This course explores how to achieve seamless data interoperability between systems using Databricks Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats. Participants will learn to implement, manage and optimize a data environment where tables can be accessed by both Delta and Iceberg clients, enabling cross platform analytics and eliminating data silos.

Advanced Techniques with Spark Declarative Pipelines

This course explores Databricks' Lakeflow Spark Declarative Pipelines (SDP) for building production-grade streaming pipelines. You will learn advanced design patterns, robust data quality enforcement, and cross-platform integration essential for real-world lakehouse engineering.

Throughout the course, you will dive into modern data ingestion and processing techniques, mastering tools like Liquid Clustering for layout optimization and the Multiplex Streaming pattern for mixed-schema events. By the end of the modules, you will know how to confidently handle schema evolution, automate Change Data Capture (CDC), and ensure data integrity.

Through lectures and hands-on demos, you will:

• Build multi-flow pipelines to ingest multi-source data into a unified Bronze table.

• Apply Liquid Clustering and Data Quality Expectations across Silver and Gold layers.

• Implement the Multiplex pattern with Iceberg UniForm for cross-platform data access.

• Automate SCD Type 2 history tracking using AUTO CDC INTO.

• Design zero-data-loss quarantine pipelines to audit and manage invalid records.

Note:

1. This course is the first in the 'Advanced Data Engineering with Databricks' series.

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Data Modeling Strategies

In this course, you will learn how to design scalable, analytics-ready data models using the Databricks Lakehouse Platform. The course is structured into three modules covering foundational data modeling principles, advanced warehousing and machine learning use cases, and modern data product design. You’ll explore core methodologies like Inmon’s CIF, Kimball’s dimensional modeling, and Data Vault 2.0, along with practical demos on implementing these strategies in Databricks.

Through hands-on labs, you’ll build ER diagrams, apply SCD Type 2 transformations, and create feature stores for ML workflows. The course also introduces the concept of data products and how product thinking, governance, and semantic consistency enable scalable cross-domain integration. By the end of the course, you’ll be equipped to model, manage, and operationalize data effectively in a unified Lakehouse architecture.

Note: The course includes practice labs that the learners should perform after going through the entire course.

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

Paid & Subscription

Lab

Associate