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Databricks Data Privacy

This content provides a comprehensive guide to managing data privacy within Databricks. It covers key topics like Delta Lake architecture, regional data isolation, GDPR/CCPA compliance, and Change Data Feed (CDF) usage. Through practical demos and hands-on labs, participants learn to use Unity Catalog features for securing sensitive data and ensuring compliance, empowering them to safeguard data integrity effectively.


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

Skill Level
Professional
Duration
4h
Prerequisites

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

• Ability to perform basic code development tasks using the Databricks Data Engineering & Data Science workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc)

• Intermediate programming experience with PySpark

• Extract data from a variety of file formats and data sources

• Apply a number of common transformations to clean data

• Reshape and manipulate complex data using advanced built-in functions

• Intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.)

• Beginner experience configuring and scheduling data pipelines using the Lakeflow Pipelines Editor

• Beginner experience defining Apache Spark™ Declarative Pipelines using PySpark

• Ingest and process data using Auto Loader and PySpark syntax

• Process Change Data Capture feeds with APPLY CHANGES INTO syntax

Outline

1. Storing Data Securely
• Regulatory Compliance
• Data Privacy
2. Unity Catalog
• Key Concepts and Components
• Audit Your Data
• Data Isolation
• Securing Data in Unity Catalog
3. PII Data Security
• Pseudonymization & Anonymization
• Summary & Best Practices
• PII Data Security
4. Streaming Data and CDF
• Capturing Changed Data

• Deleting Data in Databricks
• Processing Records from CDF and Propagating Changes
• Propagating Changes with CDF Lab

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Nov 18
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Nov 18
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Nov 18
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 20
09 AM - 01 PM (Asia/Singapore)
-
English
$750.00
Jan 20
09 AM - 01 PM (Europe/Paris)
-
English
$750.00
Jan 20
01 PM - 05 PM (America/New_York)
-
English
$750.00

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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.