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Data Analysis with Databricks

This course provides a comprehensive introduction to Databricks SQL. Learners will ingest data, write queries, produce visualizations and dashboards, and configure alerts. This course will prepare you to take the Databricks Certified Data Analyst Associate exam.

This course consists of two four-hour modules. 


Note: the 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.

SQL Analytics on Databricks

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights.


You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL.

 

Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

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
Skill Level
Associate
Duration
8h
Prerequisites

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


SQL Analytics on Databricks

• A working knowledge of using SQL for data analysis purposes. 

• Able to locate and inspect datasets using Unity Catalog and Catalog Explorer.

• Ability to execute code in notebooks in perform basic operations.

• Understanding of data structures including tables, schemas, and views; familiarity with Medallion Architecture and Delta Lake concepts.

• A basic understanding of statistical analysis.

• Understand the structure and defining characteristics of specific data formats such as CSV, JSON, TXT, and Parquet.

• Be familiar with the user interface of the Databricks Data Intelligence Platform.


AI/BI for Data Analysts

• 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

1. SQL Analytics on Databricks

Data Discovery

• Unity Catalog for Data Discovery and Ownership

• Demo: Data Discovery in Unity Catalog

• Lab: Use Unity Catalog to Locate and Inspect Datasets


Data Importing

• Ingesting Data into Databricks

• Demo: Uploading Data to Databricks Using the UI

• Demo: Programmatic Exploration and Data Ingestion to Unity Catalog

• Lab: Import Data into Databricks


SQL Execution

• Databricks SQL and Databricks SQL Warehouses

• Demo: The Unified SQL Editor

• Demo: Manipulate and Transform Data with Databricks SQL

• Demo: Creating Views with Databricks SQL

• Lab: Manipulate and Analyze a Table


Query Analysis

• Databricks Photon and Optimization in Databricks

• Demo: Query Insights

• Best Practices for SQL Analytics


2. AI/BI for Data Analysts

• 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

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Oct 27 - 28
01 PM - 05 PM (America/New_York)
-
English
$1000.00
Nov 17
10 AM - 06 PM (Asia/Singapore)
-
English
$1000.00
Nov 17 - 18
01 PM - 05 PM (Europe/Paris)
-
English
$1000.00
Nov 17
09 AM - 05 PM (America/New_York)
-
English
$1000.00

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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.