Redshift com software#
This can be done by leveraging metadata from other sources, like an existing data catalog or other type of software in the data stack, and Immuta’s sensitive data discovery. Step 1: Discover and Tag DataĪfter registering a data source from Redshift or Databricks, we’ll first want to discover and tag the data to identify any sensitive attributes or categories, including state. In practice, we will aim to ensure that when users run the query below, they will only be able to see data from their own state. This is an example of segmenting data across platforms for multi-tenancy, in which each state is a tenant. In this scenario, we’ll look to consistently segment HR data by state for all tables across an AWS data architecture with Redshift and Databricks, so that we can comply with internal data use rules. Here, we’ll look step-by-step at how Immuta provides centralized, fine-grained access control across different cloud data platforms in a lakehouse architecture. But the need to consistently implement data access control across this open architecture approach can be elusive. Organizations are increasingly adopting data lakehouse architectures to reap the benefits of added flexibility, scalability, and cost-efficiency that they provide. Enforcing Redshift Attribute-Based Access Control
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