Data Architect

DATB-GSLA : Get Started with Lakehouse Architecture on Databricks

Course Duration

2 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

In this course, you will explore the Databricks Data Intelligence Platform from the perspective of platform architecture, specifically related to the platform foundation in lakehouse architecture. You will learn about the scope, vision, and capabilities of a platform founded in lakehouse architecture, with a focus on how Databricks integrates with a cloud platform’s architecture. You’ll learn about the key features of a successful lakehouse implementation, specifically how to adhere to the well-architected lakehouse framework, which emphasizes structural excellence through specific dimensions, principles and best practices. You’ll also learn about data architecture strategy for the acceleration of data and AI endeavors.

What You'll Learn

Databricks Overview
Exploring Lakehouse Architecture
Building a Well-Architected Lakehouse

Who Should Attend

Professionals with foundational exposure to data-engineering concepts (such as ingestion, transformation, data modelling) seeking to understand modern data architecture.
Data engineers, analytics engineers, platform architects or technical leads tasked with evaluating, designing or modernising a lakehouse solution (combining data-lake and data-warehouse approaches) on the Databricks platform.
Individuals looking to understand how the Databricks Lakehouse architecture works: the scope, vision, foundational principles, integration with major cloud providers, and how to apply the well-architected lakehouse framework.
Team members responsible for setting or executing data/AI platform strategies—who will help accelerate organisational-wide analytics, data-engineering and AI initiatives by leveraging a modern lakehouse.
Practitioners who already have basic familiarity with data platforms and now wish to build their understanding of architecture, strategy and best practices (rather than deep hands-on implementation) on Databricks.

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

A basic understanding of data engineering principles and topics such as data collection, extraction, ingestion, and transformation.
Prior experience in data platform solutions architecture or similar areas of study.

Learning Journey

Coming Soon...

Module 1. Databricks Overview

Databricks Infrastructure
Databricks Data Intelligence Platform
Unity Catalog Overview
Databricks Workspace Walkthrough

Module 2. Exploring Lakehouse Architecture

The Scope of Lakehouse Architecture
The Modern Data and AI Platform
Exploring the Guiding Principles of the Lakehouse

Module 3. Building a Well-Architected Lakehouse

Integrating Databricks with Cloud Provider Environments
A Well-Architected Lakehouse Framework

Frequently Asked Questions (FAQs)

None

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