Because geo-replication is asynchronous, it is possible that data written to the primary region has not yet been written to the secondary region at the time an outage occurs. The Last Sync Time property indicates the last time that data from the primary region was written successfully to the secondary region. All writes made to the primary region before the last sync time are available to be read from the secondary location. Writes made to the primary region after the last sync time property may or may not be available for reads yet.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/last-sync-time-get

Scenario: Implement a surrogate key to account for changes to the retail store addresses.
A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-identity

Explanation:
Storage1 provides a hierarchical namespace: Yes
Files from March 2025 will be included: No
Only files that have a Parquet file extension will be included: Yes
Query Breakdown
* Data Source:
* The OPENROWSET function queries data stored in Azure Data Lake Storage Gen2 (storage1) using the serverless SQL pool in Synapse Analytics.
* The data is stored in Parquet files in the folder structure data/orders/year=YYYY/month=MM/.
* Query Filter:
* The filter conditions in the query are:
* customerorders.filepath(1) = '2024': Limits the query to files in the folder year=2024.
* customerorders.filepath(2) IN ('3', '4'): Limits the query to files in the subfolders month=3 or month=4.
* File Format:
* The FORMAT = 'parquet' clause specifies that only Parquet files will be queried.
Statements Analysis
* Storage1 provides a hierarchical namespace.aswer: Yes
* Azure Data Lake Storage Gen2 supports a hierarchical namespace, which enables folder-based organization.
* The folder structure (e.g., data/orders/year=2024/month=3/) demonstrates the use of a hierarchical namespace.
* Files from March 2025 will be included.aswer: No
* The query explicitly filters for year=2024, so files from 2025 will not be included in the results.
* Only files that have a Parquet file extension will be included.aswer: Yes
* The FORMAT = 'parquet' clause in the query ensures that only Parquet files are queried. Files with other extensions (e.g., .csv or .json) will not be included.

Materialized views for dedicated SQL pools in Azure Synapse provide a low maintenance method for complex analytical queries to get fast performance without any query change.
Note: When result set caching is enabled, dedicated SQL pool automatically caches query results in the user database for repetitive use. This allows subsequent query executions to get results directly from the persisted cache so recomputation is not needed. Result set caching improves query performance and reduces compute resource usage. In addition, queries using cached results set do not use any concurrency slots and thus do not count against existing concurrency limits.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuning-materialized-views
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuning-result-set-caching

A shared access signature (SAS) provides secure delegated access to resources in your storage account. With a SAS, you have granular control over how a client can access your data. For example:
What resources the client may access.
What permissions they have to those resources.
How long the SAS is valid.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview