Databricks Data Privacy
Databricks Data Privacy teaches data engineers how to find, protect, delete and account for personal data on the Databricks Data Intelligence Platform. It treats privacy as a set of technical decisions and controls built on Unity Catalog and Delta Lake. The course separates privacy from security, governance and compliance, then organizes the work around an identify, protect, manage and verify lifecycle and a short decision model that learners apply to every technique.
Learners start by finding sensitive columns with Data Classification and recording how each should be treated with a governed tag. They protect those columns with tag-driven attribute-based access control (ABAC) policies and use manual column masks and row filters where a central policy doesn't fit. De-identification goes further with salted hashing, tokenization through a separately governed vault and column encryption, with the salt and key held in Databricks Secrets. For the right to erasure, learners track a request, find the subject's data with classification and lineage, delete and propagate the change with DELETE and MERGE, then purge the underlying files with REORG TABLE ... APPLY (PURGE) and retention-aware VACUUM. The course closes by combining lineage, audit logs, query history and Delta table history into an evidence trail that shows whether each control worked.
All data is synthetic. The demos follow one multinational retailer's data estate through the lifecycle and end each control with evidence that it worked, such as a masked query result, an empty erasure check or a table history entry. Two scenario-based labs reinforce the workflow. In the first, learners repair broken protection on a subsidiary's customer table. In the second, they finish a half-completed erasure request and assemble the evidence that it is complete. The course is technical and does not give legal advice.
Note:
1. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.
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
In this course, the content was developed for participants with these skills/knowledge/abilities:
• Basic familiarity with the Databricks workspace, including navigating it, opening notebooks and running notebook cells
• Familiarity with Unity Catalog concepts, including catalogs, schemas, tables and granting privileges
• Intermediate SQL, including joins, aggregations and MERGE statements
• Basic familiarity with Delta Lake tables, including table history and time travel
• Familiarity with layered data pipelines such as the medallion (bronze, silver, gold) architecture
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
Registration options
Databricks has a delivery method for wherever you are on your learning journey
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
Register now
Instructor-Led
Public and private courses taught by expert instructors across half-day to two-day courses
Register now
Blended Learning
Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase
Purchase now
Skills@Scale
Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details
