Get Started with Databricks: End to End

This course is a comprehensive, hands-on introduction to the Databricks Data Intelligence Platform. You will follow a single end-to-end journey that starts in the workspace and finishes with an introduction to AI and machine learning, covering the everyday skills you need to work with data on Databricks.

The course covers:

• Navigating the Databricks workspace, compute options, and the Lakehouse versus Genie experiences.

• Understanding and ingesting data with Unity Catalog, including file upload and the Catalog Explorer.

• Working in Databricks notebooks and using Genie Code to write and debug code.

• Querying data with SQL, managing saved queries, and building dashboards and visualizations.

• Exploring data in natural language with Genie Agents.

• An introduction to AI and machine learning with MLflow and the Data Science Agent.

By the end of the course, you will be comfortable navigating Databricks, finding and governing data in Unity Catalog, writing SQL and notebook code with AI assistance, building dashboards, and exploring data with Genie.

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.

Languages Available: English | 日本語 | 한국어

Skill Level

Onboarding

Duration

Prerequisites

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

• Basic computer and web browser skills. You can navigate a web application and work with tabs and menus.

• A willingness to work with data. No prior Databricks experience is required.

• Helpful, but optional: basic SQL. Familiarity with SELECT statements makes the SQL and dashboard modules easier, but the course introduces what you need as you go.

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

AI Engineer

Prompt Engineering Fundamentals - Mandarin Chinese

提示公司 AI Assistant 的两部分基础课程。第一部分（基础知识）阐释提示的具体程度如何影响回答质量，将该助手定位为一位才华横溢但严格按字面理解指令、通过 RAG 架构扎根于内部知识的"实习生"，介绍其可访问与不可访问的内容，强调"信任但需核实"的思维方式，并引入 C-O-I-E 框架（Context、Outcome、Instruction、Example）。第二部分（技巧）构建提示技巧工具箱，并讲授如何在各技巧之间做出选择：将任务与方法相匹配、内容生成（零样本与少样本）、推理（思维链、自问自答、元提示）、工作流（提示链接以及何时使用链接而非单一提示），以及如何清晰地组织提示结构。

Free

Introductory

Get Started with Lakebase - Mandarin Chinese

本入门课程介绍 Databricks Lakebase——一项内置于 Databricks Data Intelligence Platform 的全托管 PostgreSQL 服务，旨在将运营型（OLTP）与分析型（OLAP）工作负载更紧密地结合在一起。

课程首先通过概念讲解，对比 OLTP 与 OLAP 系统，阐述二者在性能特征、存储模型及典型应用场景上的差异。您还将了解组织在同时维护独立的事务型数据库与分析平台时所面临的挑战，包括数据移动、延迟以及架构复杂性等问题。

随后，您将学习 Databricks Lakebase 如何通过提供兼容 PostgreSQL 的运营数据库来应对上述挑战——该数据库可直接与 Databricks 湖仓集成，使运营应用与分析工作能够在统一平台内协同运行。

本课程为入门级课程，重点在于理解 Lakebase 的核心概念及基本工作流程。在 Lakebase 之上构建完整的生产应用不在本课程的讨论范围之内。

完成本课程后，您将理解 Lakebase 如何帮助组织在由 Unity Catalog 和 Databricks 湖仓驱动的单一治理平台内，实现运营系统与分析能力的融合贯通。

您可以创建一个 Databricks Free Edition 账户，在动手实践环境中尝试这些跟随式演示。

注意：Databricks Academy 正在将 Databricks 环境中的课堂课程转为基于 notebook 的形式，并停止在讲座中使用幻灯片。您可以在 Vocareum 实验环境中访问课程 notebook。

Prompt Engineering Fundamentals

A two-part foundation in prompting a company AI Assistant well. Part 01 (Fundamentals) explains how prompt specificity shapes answer quality, frames the assistant as a brilliant but literal intern grounded in internal knowledge through a RAG architecture, covers what it can and cannot access, stresses a trust-but-verify mindset, and introduces the C-O-I-E framework (Context, Outcome, Instruction, Example). Part 02 (Techniques) builds a toolkit of prompting techniques and teaches how to choose among them: matching tasks to approaches, content generation (zero-shot and few-shot), reasoning (chain-of-thought, self-ask, meta-prompting), workflow (prompt chaining and when to chain versus use a single prompt), and clearly structuring prompts.

Free

Introductory