Hortonworks 使数据管理员可以确保 Data Lake 及其包含的资产的透明性、可再现性、可审计性和一致性。Apache Atlas 现在可以可视化跨组件沿袭，提供跨各种分析引擎（如 Apache Storm、Kafka、Falcon 和 Hive）的完整数据移动视图。数据管理员、操作和合规性人员现在可以可视化数据集的沿袭，然后向下钻取与运营、安全和证据相关的详细信息。在平台级别进行此跟踪时，使用多个引擎的任何应用程序将在本地跟踪。这可将洞察力超越单个应用程序视图。
Through the combination of Atlas and Ranger companies can create a flexible security profile that meets the needs of data-driven enterprises.
A data asset such as a table or column can be marked with the metadata tag related to compliance or business taxonomy (such as "PCI"). This tag is then used to assign permission to a user group.
Administrators can customize entitlements based on geography. A user trying to access the same data from different locations would be subject to unique geographical context thereby triggering access based on different set of privacy rules.
Data Expiry-based Policy
Apache Atlas can assign expiration dates to a data tag. Apache Ranger would inherit the expiration date and automatically deny users access to the tagged data after the expiration date. This policy is relevant for business use cases where data can become toxic after an expiry date.
It is now possible to define a security policy that restricts combining two data sets. Administrators can now apply a metadata tag to both data sets to prevent them from being combined, helping avoid privacy violations.
Cross Component Lineage with Apache Atlas across Apache Sqoop, Hive, Kafka & Storm
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