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.

Skill Level

Introductory

Duration

Prerequisites

- 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 Delta Live Tables (DLT) UI
- Beginner experience defining Delta Live Tables pipelines using PySpark
- Ingest and process data using Auto Loader and PySpark syntax
- Process Change Data Capture feeds with APPLY CHANGES INTO syntax
- Review pipeline event logs and results to troubleshoot DLT syntax

Outline

Course Introduction
Storing Data Securely
Regulatory Compliance
Data Privacy
Unity Catalog
Key Concepts and Components
Audit Your Data
Data Isolation
Securing Data in Unity Catalog
PII Data Security
Pseudonymization & Anonymization
Summary & Best Practices
PII Data Security
Streaming Data and CDF
Capturing Changed Data

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

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