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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
Dec 08
09 AM - 05 PM (Europe/Paris)
-
English
$1000.00
Dec 15
09 AM - 05 PM (Europe/Paris)
-
English
$1000.00
Jan 19
09 AM - 05 PM (Australia/Sydney)
-
English
$1000.00
Jan 19
10 AM - 06 PM (Asia/Kolkata)
-
English
$1000.00
Jan 19
09 AM - 05 PM (Europe/London)
-
English
$1000.00
Jan 19
09 AM - 05 PM (America/Los_Angeles)
-
English
$1000.00

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.

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Upcoming Public Classes

Data Engineer

Databricks Performance Optimization - Mandarin Chinese

Databricks Performance Optimization 课程教导数据工程师和分析师如何在 Databricks Data Intelligence Platform 上诊断、衡量和修复性能瓶颈,以及如何将性能提升与成本关联起来。本课程遵循"先衡量、先平台"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,学习如何衡量性能。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,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优化效果。两个基于场景的实验室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 | Français | Español | 普通话

Paid
4h
Lab
instructor-led
Professional

Databricks Data Privacy - Mandarin Chinese

Databricks 数据隐私课程面向数据工程师,讲解如何在 Databricks Data Intelligence Platform 上发现、保护、删除个人数据并对其负责。课程将隐私视为建立在 Unity Catalog 和 Delta Lake 之上的一组技术决策与控制。课程把隐私与安全、治理和合规区分开来,然后围绕识别、保护、管理、验证的生命周期,以及学习者可应用于每项技术的简短决策模型来组织工作。

学习者首先使用 Data Classification 查找敏感列,并用治理标签记录每列应如何处理。他们用基于标签的属性访问控制(ABAC)策略保护这些列,并在中央策略不适用时使用手动列掩码和行过滤器。去标识化进一步涵盖加盐哈希、通过单独治理的保险库进行令牌化,以及列加密,盐值和密钥保存在 Databricks Secrets 中。对于删除权,学习者跟踪请求,借助分类和血缘查找主体数据,用 DELETE 和 MERGE 删除并传播变更,再用 REORG TABLE ... APPLY (PURGE) 和考虑保留期的 VACUUM 清除底层文件。课程最后将血缘、审计日志、查询历史和 Delta 表历史组合成证据链,证明各项控制是否生效。

所有数据均为合成数据。演示沿着一家跨国零售商的数据资产走完生命周期,并以已掩码的查询结果、空的删除检查或表历史记录等证据结束每项控制。两个基于场景的实验强化该工作流。第一个实验中,学习者修复子公司客户表上损坏的保护。第二个实验中,他们完成一份只做了一半的删除请求,并汇总完成证据。本课程为技术课程,不提供法律建议。

注意: Databricks Academy 正在将 Databricks 环境中的课堂教学过渡为基于 Notebook 的形式,不再使用幻灯片进行讲座。您可以在 Vocareum 实验室环境中访问讲座 Notebook。

Paid
4h
Lab
instructor-led
Professional

Questions?

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