AI/BI for Data Analysts

This course teaches data analysts how to design, build, publish, and operate AI/BI Dashboards in Databricks. AI/BI Dashboards combine governed Unity Catalog data with interactive visualizations, filters, and Genie integration so business users can explore answers without writing code.

The course follows a single end-to-end build. You start with source tables in Unity Catalog and finish with a published, monitored multi-page dashboard. Along the way you learn how dashboards fit into the broader Databricks AI/BI product family and where Genie, datasets, visualizations, and filters each fit in the workflow.

The content covers:

• AI/BI Dashboard fundamentals and how they relate to Genie and the rest of the Databricks platform.

• Exploring source data in Unity Catalog and designing reusable dashboard datasets with SQL.

• Authoring visualizations (KPIs, trends, breakdowns) and laying out a clean multi-page dashboard.

• Using Genie Code to draft SQL, charts, and filters from natural language prompts.

• Adding filters to make dashboards interactive and responsive to viewer questions.

• Publishing, sharing, and managing permissions so the right people can view and edit the dashboard.

• Running the dashboard in production with scheduled refresh, caching, and usage monitoring.

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

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

• Basic familiarity with the Databricks workspace. You can navigate the workspace UI, open and use notebooks, and attach a SQL warehouse or other compute resource.

• Basic familiarity with Unity Catalog. You understand the three-level namespace (catalog, schema, table) and can find a table in the catalog explorer.

• Working knowledge of SQL, including joins. You can read and write SELECT statements with WHERE, GROUP BY, and aggregates, and you can join two tables on a shared key.

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Self-Paced

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

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

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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

Get Started with Databricks for Generative AI - Mandarin Chinese

本课程提供对 Mosaic AI 平台的实用入门介绍，重点讲解用于构建和部署生成式 AI 系统的核心组件与功能。学员将了解 Databricks 如何助力开发可扩展的生成式 AI 解决方案，并探索 Mosaic AI 的相关工具，例如 Vector Search、Agent Framework，以及 MLflow 用于模型跟踪与记录的生成式 AI 功能。本课程包含动手实践，内容涵盖构建并评估检索增强生成 (RAG) 管道、部署生成式 AI 智能体，以及利用评估框架优化性能。完成本课程后，学员将掌握使用 Mosaic AI 设计、部署和监控常见生成式 AI 应用所需的技能。

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

Paid & Subscription

Lab

Onboarding

Catalog Management and Data Organization

In this course, you will learn how to design, implement, and govern catalog structures and large-scale data organization on the Databricks Data Intelligence Platform. It offers a comprehensive view of Unity Catalog as the centralized governance layer for an enterprise lakehouse. Divided into five modules, it begins by placing Unity Catalog within the cloud deployment model — covering the Account Console, metastore creation, and the administrator role hierarchy. You will then translate organizational topology (business units, regions, and dev/QA/prod environments) into a scalable catalog and schema design using naming conventions, ownership patterns, and MANAGE delegation. The course then covers secure storage integration with storage credentials, external locations, the managed-storage hierarchy, managed versus external tables, and UC Volumes for non-tabular data. Next, you will apply access patterns and isolation strategies — the three-level GRANT chain, workspace-catalog binding, and schema-level Attribute-Based Access Control (ABAC) policies — to enforce fine-grained data protection at scale. Finally, the course closes with best practices for catalog design, automation, least-privilege permissions, and group-based access management.

Data Modeling Strategies

This course walks practitioners through the full spectrum of data modelling approaches on the Databricks Data Intelligence Platform - from classical data warehouse techniques (Inmon, Kimball, Data Vault 2.0), through Feature Store-driven ML use cases, to productising data via Data Products on Unity Catalog.

Each modelling approach is introduced with a lecture, then reinforced with a hands-on demo against a shared dataset (TPC-H samples). The course finishes with a comprehensive end-to-end lab that exercises ERM, dimensional modelling, Data Vault 2.0, and the Feature Store in a single integrated workflow.

Note:

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

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.

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

Paid & Subscription

Lab

Associate