Databricks Data Sharing & Collaboration - Mandarin Chinese

This hands-on course shows how to share live data securely with OpenSharing (formerly Delta Sharing), the open protocol from Databricks for data sharing, without copying or moving it. You work from both sides of every share, as the provider and as the recipient, in two Databricks Workspace Environments in different regions, and with an open client for Databricks-to-Open (D2O) sharing. You implement Databricks-to-Databricks (D2D) and D2O sharing, manage open recipients, read incremental changes with Change Data Feed (CDF), replicate data to avoid egress with a regional replica, and give each recipient its own slice of data with dynamic views and partition filtering. The course closes with the performance, auditing, and cost considerations of data sharing.

Note: 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.

In this course, you will learn the fundamentals of platform administration on the Databricks Data + AI Platform, with a focus on Unity Catalog and centralized governance for data and AI assets. The course follows one numbered sequence of lectures and demos, followed by a hands-on lab. You will explore the Databricks workspace and account console, data-governance principles, Unity Catalog concepts and administrator roles, identity and metastore management, and compute resources including classic compute, serverless compute, and SQL warehouses. You will then implement privileges and fine-grained access control with row filters, column masks, dynamic views, and attribute-based access control (ABAC). By the end, you will be prepared to administer Unity Catalog, manage compute resources, and apply secure, scalable data-governance practices.

Note: 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.

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。