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 to secure sensitive data and ensure compliance, empowering them to safeguard data integrity effectively.
Note:
1. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!
2. This course is the second in the 'Advanced Data Engineering with Databricks' series.
Languages Available: Languages Available: English | 日本語 | Português BR | 한국어
- 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
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