Create Your First Workspace Using Databricks Express

In this course, you will explore the core features and functionalities of Databricks Express Setup, a streamlined way to get started with Databricks. The course is designed to help you quickly set up and navigate a serverless workspace while providing a comprehensive understanding of the credit-based trial system, including the $400 allowance. Divided into six modules, it starts with an introduction to Databricks Express Setup, followed by an exploration of its key features and benefits. You will learn to create and manage serverless workspaces, perform exploratory data analysis using Unity Catalog, and gain insights into collaboration through data sharing.

Additionally, you’ll be guided through trial management and upgrade options, ensuring you can effectively transition from trial to paid accounts. The course also includes an internal-only module on the evolution of trial credits and account activation methods. By the end of the course, you will have a solid foundation in using Databricks Express Setup for data and AI workloads, enabling you to confidently explore and analyze data, collaborate with peers, and manage your Databricks environment efficiently.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Skill Level

Introductory

Duration

1h 30m

Prerequisites

- Basic knowledge of cloud computing and SQL concepts such as networking basics, SQL commands, aggregate functions, filters and sorting, indexes, tables, and views.

- Basic knowledge of Python programming, Jupyter notebook interface, and PySpark fundamentals.

Outline

Module 1: Introduction and Onboarding to Databricks Express

- Getting Started with Databricks Express

- Setting Up Your Databricks Express Account

Module 2: Express Setup and Workspace Navigation

- Exploring Serverless Workspaces and Data Intelligence

- Managing Storage and Data Sharing in Databricks

Module 3: Trial Management and Upgrade Options

- Exploring Credit-Based Trials in Databricks

- Setting Up Your Databricks Express Account

Module 4: Navigating Databricks Workspace and Data Sharing

- Demo: Databricks Workspace Navigation

- Demo: Data Management in Databricks Workspace

- Demo: Exploratory Data Analysis

- Demo: Delta Sharing Between Databricks Express Accounts

Self-Paced

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

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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

Model Development at Scale

In this course, you will learn to design and implement machine learning workflows that scale from a single machine to distributed compute on Databricks. The curriculum is organized into three modules: foundations of model development (when and why to distribute, and how Spark executes work), scalable model development with Spark ML and MLflow, and distributed model training with Ray and PyTorch — closing with a framework for choosing the right tool for a given workload.

Through hands-on demos and labs, you will build distributed Spark ML pipelines using Transformers, Estimators, and the fit/transform paradigm; scale existing pandas code with the Pandas API on Spark; and track, compare, and govern experiments with MLflow 3 and Unity Catalog. You will then train models across a cluster with Ray Train and PyTorch (TorchDistributor and DeepSpeed), and learn to match each workload to the right framework based on data size, model size, hardware, and cost.

The course concludes with a capstone that takes a problem end to end — engineering features, training a model at scale, tracking it with MLflow, and registering it in Unity Catalog with a governance alias. (Production deployment, serving, and monitoring are covered in the separate Advanced MLOps course.)

By the end, you will be able to choose, build, and justify distributed, reproducible, and governed model-development workflows on Databricks.

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.

Free

Professional

Data Engineer

Get Started with Data Governance on Databricks - Mandarin Chinese

在本课程中，您将通过动手演示和综合实验室，浏览 Unity Catalog 并了解 Databricks 上的细粒度访问控制。您将学习表类型、目录和架构配置、基于组的访问管理以及访问控制迁移策略。本课程包含以下演示：应用基于行级安全和列掩码的细粒度访问控制、基于属性的访问控制、组合控制、迁移控制，以及一个用于全面治理实施的实验室。

Languages Available: English | 日本語 | 한국어 | Português BR | Français | Español | 普通话

Free

Onboarding