In the previous entries in our Snowflake security and data privacy blog series<\/a>, we discussed how to store and organize data<\/a>, how to identify which data is sensitive, and a variety of ways to apply granular access control. In this entry, we\u2019ll discuss the importance of Snowflake roles and how they drive all the policies and techniques we\u2019ve discussed so far.<\/p>\n A \u201cRole\u201d in Snowflake controls not only what data a given user can see but also what they can do, which in turn has performance and cost impacts.<\/p>\n In traditional databases, giving someone access to a database server gives them access to both the data on that server and access to use its CPU and RAM to run queries. One of Snowflake\u2019s key features is that it treats storage and compute separately. I can give both finance and data science users access to the same storage \u2013 the same data. But I can also give finance access to a standard compute engine to run reports while giving data scientists access to an extra-large compute engine to run complex analysis or machine-learning algorithms.<\/p>\n Both compute engines can operate simultaneously, and both can query the same data without any impact on each other whatsoever \u2013 the data science routines do not slow down or interfere with the finance reports.<\/strong><\/p>\n Snowflake<\/a> calls these sets of compute power \u201cwarehouses,\u201d which is a bit confusing. A warehouse, in this context, simply refers to a set of distributed CPU and RAM \u2013 a set of virtual machines \u2013 and does not involve any data. If I want to let finance query some data, I first have to give them access to the data itself and also give them access to a warehouse (CPU and RAM) that allows them to do work on that data.<\/p>\n Snowflake, like most cloud data platforms, charges very little for the storage itself and primarily charges based on how much compute power you use. Separate compute allows you not only to isolate workloads but also to track usage and (if appropriate) charge back to the right department.<\/p>\n For this reason, as well as the security considerations described above, you should define a Role based on a common set of activities, responsibilities and behaviors, not solely on a set of permissions.<\/p>\n For example, if you create a Snowflake role called \u201cProd_Read_Only,\u201d that could describe a very wide variety of people with very different responsibilities. It also makes maintenance difficult \u2013 you have to add and remove specific people from a whole variety of \u201croles\u201d (Prod_Read_Only, Dev_Read_Write, UAT_Read_Write) as their needs change. Further, it only describes their data access \u2013 what if you want both finance and data scientists to have \u201cProd_Read_Only\u201d access but want them to use different warehouses?<\/strong><\/p>\n Roles should instead be specific to their real-world position. \u201cData_Developer,\u201d \u201cApplication_Developer,\u201d \u201cData_Tester,\u201d \u201cFinance_Analyst,\u201d \u201cFraud_Analyst,\u201d \u201cBroker,\u201d \u201cSupplier\u201d and so on \u2013 all have a need to access different, but overlapping, data sets in different ways. Even non-human roles exist, such as \u201cData_Loader\u201d or \u201cData_Monitor\u201d for automated routines.<\/p>\n After you define your roles, it\u2019s relatively easy to define and maintain their access to both processing power and data, including all the granular security options<\/a> we\u2019ve discussed in this series. For example, your role descriptions might look like this:<\/p>\n Data Developer:<\/strong><\/p>\n Application Tester:<\/strong><\/p>\n Data Monitor (non-human):<\/strong><\/p>\n Several roles may have the same permissions. This is fine and not wasted effort: at any moment, it may make sense to remove Prod read access from the data testers but keep it for the application testers, at which point you\u2019ll be happy you kept them separate. At the same time, perhaps you\u2019ll have all those development roles share a single warehouse until you find conflicting needs demand a second.<\/strong><\/p>\n Snowflake roles can easily be inherited and have their permissions mixed and matched as needed. Consider a hierarchy like this:<\/p>\n In this example, perhaps all members of the \u201cFinance_Analyst\u201d role have access to sensitive financial columns (unmasked), but none have access to PII columns. Members of Finance-US can see US rows, and all US finance analysts share a single medium-powered warehouse for querying. Members of individual US regions have further row filtering but still share the same warehouse with all their US colleagues.<\/p>\n Inheritance also makes it easy to manage temporary access.<\/strong> Perhaps the data support team, who cannot normally see Prod data, needs temporary read access in Prod to investigate a mismatch in sensitive data alerted by the data monitor role. You can temporarily add one person \u2013 or the entire role \u2013 under \u201cData_Monitor\u201d and inherit all its access while they investigate the problem. Then, when they finish investigating, you can remove them in a single step.<\/p>\nUsing Snowflake Roles to Determine Access Control<\/h2>\n
Snowflake Roles in Practice<\/h2>\n
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Moving Forward With Snowflake Roles<\/h2>\n