![]() ![]() Important: Once started, a dedicated SQL pool consumes credits in your Azure subscription until it is paused. Continue to the next exercise while the dedicated SQL pool resumes.It will take a minute or two to resume the pool. If the SQLPool01 dedicated SQL pool is paused, hover over its name and select ▷. You should have paused the SQL pool at the end of the previous lab, so resume it by following these instructions: This lab uses the dedicated SQL pool you created in the previous lab. If desired, you also have the option of applying Dynamic Data Masking to mask sensitive data returned in queries on a column by column basis.īefore starting this lab, you must complete at least the setup steps in Lab 4: Explore, transform, and load data into the Data Warehouse using Apache Spark. ![]() Determine at the table level what data should be hidden from specific groups of users then define security predicates to apply row level security (filters) on the table. Identify the columns representing sensitive data, then secure them by adding column-level security. Introspect the data that is contained within the SQL Pools in the context of potential sensitive/confidential data disclosure. Leverage Azure Key Vault to store sensitive connection information, such as access keys and passwords for linked services as well as in pipelines. This lab will guide you through several security-related steps that cover an end-to-end security story for Azure Synapse Analytics. Secure Azure Synapse Analytics workspace data. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |