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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: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.


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

Skill Level
Associate
Duration
4h
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.

Outline

• Demo: REQUIRED - Data Setup and Exploration

• Introduction to AIBI Dashboards

• Demo: Designing Datasets for Dashboards

• Demo: Page 1 Visualizations - KPIs and Yearly Trends

• Demo: Page 1 Visualizations - Province Breakdowns

• Lab: Build a Dashboard - NYC Taxi Data

• Demo: Genie Code for Dashboards - Building Page 2

• Demo: Adding Filters to Your Dashboard

• Demo: Publishing and Sharing Your Dashboard

• Demo: Running Your Dashboard in Production

• Summary and Next Steps

• Lab: Extend and Share Your Taxi Dashboard 

• Append A: Bonus - Dashboard Caching Overview

• Append B - Bonus - Local Metric Views

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Nov 11
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Nov 11
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Nov 12
09 AM - 01 PM (America/Los_Angeles)
-
English
$750.00
Dec 09
09 AM - 01 PM (Asia/Kolkata)
-
English
$750.00
Dec 09
01 PM - 05 PM (Europe/Paris)
-
English
$750.00
Dec 09
09 AM - 01 PM (America/New_York)
-
English
$750.00
Jan 13
09 AM - 01 PM (Asia/Singapore)
-
English
$750.00
Jan 13
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Jan 13
01 PM - 05 PM (America/New_York)
-
English
$750.00

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

Private Class Request

If your company is interested in private training, please submit a request.

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Upcoming Public Classes

Databricks Performance Optimization - Mandarin Chinese

Databricks Performance Optimization 课程向数据工程师和分析师讲授如何在 Databricks Data Intelligence Platform 上诊断、衡量和修复性能瓶颈,以及如何将性能改进与成本关联起来。本课程遵循"先衡量、先利用平台"的工作流:让平台的自动优化功能承担繁重工作,通过 Query Profile 和系统表验证已应用的优化,再在必要时进行手动调优。

学员将以 Query Profile、Performance Insights 和查询历史记录系统表为基础,建立衡量性能的能力。在此基础上,他们将探索关键优化技术,包括数据布局与自调优托管表、liquid clustering、缓存与中间结果、shuffle、数据倾斜、溢出、行爆炸、驱动程序性能、Python UDF、serverless compute、Photon 以及成本归因。

在整个课程中,学员将针对合成零售数据中刻意设计的慢查询进行探索,并观察不同优化技术对性能的影响。他们将利用文件裁剪、任务执行时间、Photon 覆盖率和成本等依据来评估改进效果。两个基于场景的实验将强化从诊断到验证的完整优化工作流。

注意:Databricks Academy 正在将 Databricks 环境中的课堂教学转为基于 notebook 的形式,不再使用幻灯片进行授课。您可以在 Vocareum 实验环境中访问课程 notebook。

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

Paid
4h
Lab
instructor-led
Professional

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.