Get Started with Databricks Platform Administration

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。

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

This course introduces Databricks Genie One, an AI-powered cowork environment designed around your needs and work, where you can ask questions in plain language about your organization's data and get governed, secure answers, schedule reports to run automatically, and automate your workflow to get more work done faster and more accurately, as it's based on your data and business. You'll learn what Genie, Genie One, and Genie Agents are, how they all keep your data secure, and the best practices for getting the best results.