SQL Server 2019 Feature Notebooks

In this folder, you will find various notebooks that you can use in Azure Data Studio to guide you through the new features of SQL Server 2019.

The What's New article covers all the NEW features in SQL Server 2019.

Notebook List

Scalar_UDF_Inlining.ipynb - This notebook demonstrates the benefits of Scalar UDF Inlining along with how to find out which UDFs in your database can be inlined.
IQP_tablevariabledeferred.ipynb - In this example, you will learn about the new cardinality estimation for table variables called deferred compilation.
Batch_Mode_on_Rowstore.ipynb - In this notebook, you will learn about how Batch Mode for Rowstore can help execute queries faster on SQL Server 2019.

TDE_on_Standard.ipynb - This notebook demonstrates the ability to enable TDE on SQL Server 2019 Standard Edition along with Encryption Scan SUSPEND and RESUME.
TDE_on_Standard_EKM.ipynb - This notebook demonstrates the ability to enable TDE on a SQL Server 2019 Standard Edition using EKM and Azure Key Vault.

MemoryOptimizedTempDBMetadata-TSQL.ipynb - This is a T-SQL notebook which shows the benefits of Memory Optimized Tempdb metadata.
MemoryOptmizedTempDBMetadata-Python.ipynb - This is a Python notebook which shows the benefits of Memory Optimized Tempdb metadata.

Basic_ADR.ipynb - In this notebook, you will see how fast rollback can now be with Accelerated Database Recovery. You will also see that a long active transaction does not affect the ability to truncate the transaction log.
Recovery_ADR.ipynb - In this example, you will see how Accelerated Database Recovery will speed up recovery.

SQL Server Big Data Clusters - Part of our Ground to Cloud workshop. In this lab, you will use notebooks to experiment with SQL Server Big Data Clusters (BDC), and learn how you can use it to implement large-scale data processing and machine learning.
Data Virtualization using PolyBase - The notebooks in this SQL Server 2019 workshop covers how to use SQL Server as a hub for data virtualization for sources like Oracle, SAP HANA, Azure CosmosDB, SQL Server and Azure SQL Database.
train_score_export_ml_models_with_spark.ipynb -This notebooks covers how you can use Spark to create and deploy machine learning models.