Pieter
898e3a6d14
- Add a link for the ledger subfolder in sql-server-samples/README.md at master · Pietervanhove/sql-server-samples (github.com) - Change the folder name from “sqlledger” to “ledger” (that’s the official feature name now). - sql-server-samples/README.md at master · Pietervanhove/sql-server-samples (github.com) o Fix grammar in “The demo in this folder showcases the ledger feature in Azure SQL Database. The demo uses the Contoso HR web application.” o Are Az PS 5.6, SqlServer 21.1.18245, Bicep 0.4.63, SSMS 18.9.1 the versions you’ve tested with? These are older versions (the same we used when developing the AE demo). Should we recommend using newer versions? o “The ContosoHR database using General Purpose compute tier, 2 vCores, and the Gen5 hardware generation.” I’m wondering if there is a better/more economical SLO/SKU we can pick, instead of the 2-core Gen 5 database. Of course, Basic/Standard would be very cheap, but we probably should promote the new v-core model. Would serverless be a better option, e.g. “GP_S_Gen5_1”? o If we change the folder name, change sqlledger to ledger in cd "C:\sql-server-samples\samples\features\security\sql-ledger\azure-sql-database\setup" o “Open a new InPrivate browser window.” Are these instructions generic enough to accommodate browsers other than Edge? - sql-server-samples/samples/features/security/contoso-hr-sql-db at master · Pietervanhove/sql-server-samples (github.com) o This is unrelated to ledger. Could you also remove this folder? It was checked in by mistake when we added the AE demo. It is redundant/not needed.
Samples for specific SQL Server and Azure SQL features
In-Memory OLTP can significantly improve performance of transaction processing in SQL Server and Azure SQL Database. It is a memory-optimized database engine integrated into the database engine, optimized for OLTP. With In-Memory OLTP you can increase the transaction throughput by up to 30 times, depending on the specifics of the workload.
Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) enables you organization to discover and define non-transactional lists of data, and compile maintainable, reliable master lists.
SQL Server R Services (in SQL Server 2016 and above) brings R processing close to the data, allowing more scalable and more efficient predictive analytics using R in-database.
SQL Server ML Services (SQL Server 2017) brings Python processing close to the data, allowing more scalable and more efficient predictive analytics using Python in-database.
Built-in JSON functions enable you to easily parse and query JSON data stored in database, transform relational data to JSON text, and vice versa.
Built-in temporal functions enable you to easily track history of changes in a table, go back in history, and analyze historical data.
Graph tables enable you to add a non-relational capability to your database.
The SQL Server Management Objects (SMO) Framework is a set of objects designed for programmatic management of Microsoft SQL Server and Microsoft Azure SQL Database. These code snippets demonstrate features of SMO and illustrate how to use SMO properties and collections without sacrificing performance.
Samples for Business Intelligence features within SQL Server
Reporting Services provides reporting capabilities for your organziation. Reporting Services can be integrated with SharePoint Server or used as a standalone service.