Data Governance Overview
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Atlas-Ranger integration

You can use Apache Ranger with Apache Atlas to implement dynamic classification-based security policies.

Atlas provides data governance capabilities and serves as a common metadata store that is designed to exchange metadata both within and outside of the Hadoop stack. Ranger provides a centralized user interface that can be used to define, administer and manage security policies consistently across all the components of the Hadoop stack. The Atlas-Ranger integration unites the data classification and metadata store capabilities of Atlas with security enforcement in Ranger.

You can use Atlas and Ranger to implement dynamic classification-based security policies, in addition to role-based security policies. Ranger’s centralized platform empowers data administrators to define security policy based on Atlas metadata tags or attributes and apply this policy in real-time to the entire hierarchy of entities including databases, tables, and columns, thereby preventing security violations.

Ranger-Atlas Access Policies

  • Classification-based access controls: A data entity 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 permissions to a user or group. This represents an evolution from role-based entitlements, which require discrete and static one-to-one mapping between user/group and resources such as tables or files. As an example, a data steward can create a classification tag “PII” (Personally Identifiable Information) and assign certain Hive table or columns to the tag “PII”. By doing this, the data steward is denoting that any data stored in the column or the table has to be treated as “PII”. The data steward now has the ability to build a security policy in Ranger for this classification and allow certain groups or users to access the data associated with this classification, while denying access to other groups or users. Users accessing any data classified as “PII” by Atlas would be automatically enforced by the Ranger policy already defined.

  • Data Expiry-based access policy: For certain business use cases, data can be toxic and have an expiration date for business usage. This use case can be achieved with Atlas and Ranger. Apache Atlas can assign expiration dates to a data tag. Ranger inherits the expiration date and automatically denies access to the tagged data after the expiration date.

  • Location-specific access policies: Similar to time-based access policies, administrators can now customize entitlements based on geography. For example, a US-based user might be granted access to data while she is in a domestic office, but not while she is in Europe. Although the same user may be trying to access the same data, the different geographical context would apply, triggering a different set of privacy rules to be evaluated.

  • Prohibition against dataset combinations: With Atlas-Ranger integration, it is now possible to define a security policy that restricts combining two data sets. For example, consider a scenario in which one column consists of customer account numbers, and another column contains customer names. These columns may be in compliance individually, but pose a violation if combined as part of a query. Administrators can now apply a metadata tag to both data sets to prevent them from being combined.

Cross-component Lineage

Apache Atlas now provides the ability to visualize cross-component lineage, delivering a complete view of data movement across a number of analytic engines such as Apache Storm, Kafka, Falcon, and Hive.

This functionality offers important benefits to data stewards and auditors. For example, data that starts as event data through a Kafka bolt or Storm Topology is also analyzed as an aggregated dataset through Hive, and then combined with reference data from a RDBMS via Sqoop, can be governed by Atlas at every stage of its lifecycle. Data stewards, Operations, and Compliance now have the ability to visualize a data set’s lineage, and then drill down into operational, security, and provenance-related details. As this tracking is done at the platform level, any application that uses these engines will be natively tracked. This allows for extended visibility beyond a single application view.