20260309 the SQL Database Engine (#36807)

* 20260310 sql database engine

* 20260311 Microsoft SQL Database Engine

* Apply suggestions from code review

Co-authored-by: Dimitri Furman <dfurman@microsoft.com>

* 20260311 Microsoft SQL Database Engine

---------

Co-authored-by: Dimitri Furman <dfurman@microsoft.com>

Author: WilliamDAssafMSFT

azure-sql/multi-model-features.md

@@ -4,7 +4,7 @@ description: Microsoft Azure SQL enables you to work with multiple data models i
 author: WilliamDAssafMSFT
 ms.author: wiassaf
 ms.reviewer: mathoma, urmilano, damauri
-ms.date: 11/06/2024
+ms.date: 03/09/2026
 ms.service: azure-sql
 ms.subservice: service-overview
 ms.topic: concept-article
@@ -113,7 +113,7 @@ Spatial features in Azure SQL enable you to store geometrical and geographical d
 
 ## Key-value pairs
 
-Azure SQL products don't have specialized types or structures that support key-value pairs, because key-value structures can be natively represented as standard relational tables:
+Azure SQL products don't have specialized types or structures that support key-value pairs, because [key-value structures can be natively represented](https://devblogs.microsoft.com/azure-sql/azure-sql-database-as-a-key-value-store/) as standard relational tables:
 
 ```sql
 CREATE TABLE Collection (
@@ -129,6 +129,20 @@ You can customize this key-value structure to fit your needs without any constra
 
 For an example of how a relational model can be effectively used as a key-value pair solution in practice, see [How bwin is using SQL Server 2016 In-Memory OLTP to achieve unprecedented performance and scale](/archive/blogs/sqlcat/how-bwin-is-using-sql-server-2016-in-memory-oltp-to-achieve-unprecedented-performance-and-scale). In this case study, bwin used a relational model for its ASP.NET caching solution to achieve 1.2 million batches per second.
 
+## Vector
+
+Azure SQL products support the **vector** data type and vector functions underlying [modern AI applications](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications). The SQL Database Engine can store and query both structured and unstructured data, and perform vector search on that data. Vector search enables approximate nearest neighbor search using [vectors and vector indexes](/sql/sql-server/ai/vectors). 
+
+Vectors are ordered arrays of numbers (typically floating point values) that represent information about some data. For example, an image can be represented as a vector of pixel values, or a string of text can be represented as a vector of ASCII values. The process to turn data into a vector is called vectorization. The **[vector](/sql/t-sql/data-types/vector-data-type)** data type in SQL Server can store these arrays of numbers efficiently.
+
+*Embeddings* are vectors that represent important features of data. Embeddings are often learned by using a deep learning model, and machine learning and AI models utilize them as features. Embeddings can also capture semantic similarity between concepts. For example, in generating an embedding for the words `person` and `human`, we expect their embeddings (vector representation) to be similar in value since the words are also semantically similar. Once embeddings are generated, they can be stored in a SQL Server database. This allows you to store the embeddings alongside the data they represent, and to perform vector search queries to find similar data points.
+
+Azure OpenAI features models to create embeddings from text data. The service breaks text out into tokens and generates embeddings using models pretrained by OpenAI. To learn more, see [Creating embeddings with Azure OpenAI](/azure/ai-services/openai/concepts/understand-embeddings).
+
+For more information, see:
+- [Vector functions](/sql/t-sql/functions/vector-functions-transact-sql)
+- [Azure SQL Database Vector Search Samples on GitHub](https://github.com/Azure-Samples/azure-sql-db-vector-search)
+
 ## Next steps
 
 Multi-model capabilities are core SQL Server database engine features that are shared among Azure SQL products. To learn more about these features, see these articles:
@@ -138,3 +152,4 @@ Multi-model capabilities are core SQL Server database engine features that are s
 - [Spatial data in SQL Server](/sql/relational-databases/spatial/spatial-data-sql-server)
 - [XML data in SQL Server](/sql/relational-databases/xml/xml-data-sql-server)
 - [Key-value store performance in Azure SQL Database](https://devblogs.microsoft.com/azure-sql/azure-sql-database-as-a-key-value-store/)
+- [Vectors and vector indexes](/sql/sql-server/ai/vectors)

docs/big-data-cluster/big-data-options.md

@@ -80,38 +80,6 @@ For more information on building scalable architectures, see [Build a scalable s
 
 ## In-cloud
 
-### Azure SQL with Synapse
-
-You can replace the functionality of SQL Server Big Data Clusters by using one or more Azure SQL database options for operational data, and Microsoft Azure Synapse for your analytic workloads.
-
-Microsoft Azure Synapse is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems, using distributed processing and data constructs. Azure Synapse brings together SQL technologies used in enterprise data warehousing, Spark technologies used for big data, Pipelines for data integration and ETL/ELT, and deep integration with other Azure services such as Power BI, Cosmos DB, and Azure Machine Learning.
-
-Use Microsoft Azure Synapse as a replacement for [!INCLUDE [ssbigdataclusters-ver15](../includes/ssbigdataclusters-ver15.md)] when you need to:
-
-- Use both serverless and dedicated resource models. For predictable performance and cost, create dedicated SQL pools to reserve processing power for data stored in SQL tables.
-- Process unplanned or "burst" workloads, access an always-available, serverless SQL endpoint.
-- Use built-in streaming capabilities to land data from cloud data sources into SQL tables.
-- Integrate AI with SQL by using machine learning models to score data using the T-SQL PREDICT function.
-- Use ML models with SparkML algorithms and Azure Machine Learning integration for Apache Spark 2.4 supported for Linux Foundation Delta Lake.
-- Use a simplified resource model that frees you from having to worry about managing clusters.
-- Process data that requires fast Spark start-up and aggressive autoscaling.
-- Process data using.NET for Spark allowing you to reuse your C\# expertise and existing .NET code within a Spark application.
-- Work with tables defined on files in the data lake, seamlessly consumed by either Spark or Hive.
-- Use SQL with Spark to directly explore and analyze Parquet, CSV, TSV, and JSON files stored in a data lake.
-- Enable fast, scalable data loading between SQL and Spark databases.
-- Ingest data from 90+ data sources.
-- Enable "Code-Free" ETL with Data flow activities.
-- Orchestrate notebooks, Spark jobs, stored procedures, SQL scripts, and more.
-- Monitor resources, usage, and users across SQL and Spark.
-- Use Role-based access control to simplify access to analytics resources.
-- Write SQL or Spark code and integrate with enterprise CI/CD processes.
-
-The architecture of Microsoft Azure Synapse is as follows:
-
-:::image type="content" source="media/big-data-options/synapse-architecture.png" alt-text="Diagram that shows Azure Synapse architecture overview." lightbox="media/big-data-options/synapse-architecture.png":::
-
-For more information on Microsoft Azure Synapse, see [What is Azure Synapse Analytics?](/azure/synapse-analytics/overview-what-is)
-
 ### Azure SQL plus Azure Machine Learning
 
 You can replace the functionality of SQL Server Big Data Clusters by using one or more Azure SQL database options for operational data, and Microsoft Azure Machine Learning for your predictive workloads.

docs/database-engine/install-windows/install-sql-server.md

@@ -25,7 +25,6 @@ For other deployment scenarios, see:
 
 - [Installation guidance for SQL Server on Linux](../../linux/sql-server-linux-setup.md)
 - [Deploy and connect to SQL Server Linux containers](../../linux/sql-server-linux-docker-container-deployment.md)
-- [Kubernetes - Big Data Clusters](../../big-data-cluster/deploy-get-started.md) ([!INCLUDE [sssql19-md](../../includes/sssql19-md.md)] only)
 
 Beginning with [!INCLUDE [sssql16-md](../../includes/sssql16-md.md)], [!INCLUDE [ssnoversion](../../includes/ssnoversion-md.md)] is only available as a 64-bit application. Here are important details about how to get [!INCLUDE [ssnoversion](../../includes/ssnoversion-md.md)] and how to install it.
 
@@ -103,7 +102,7 @@ Other [!INCLUDE [ssnoversion](../../includes/ssnoversion-md.md)] components can
 
 | Article | Description |
 | --- | --- |
-| [Install SQL Server Database Engine](install-sql-server-database-engine.md) | Install and configure the [!INCLUDE [ssDEnoversion](../../includes/ssdenoversion-md.md)]. |
+| [Install SQL Server Database Engine](install-sql-server-database-engine.md) | Install and configure the [[!INCLUDE [ssDEnoversion](../../includes/ssdenoversion-md.md)]](../sql-database-engine.md). |
 | [Install SQL Server replication](install-sql-server-replication.md) | Install and configure [!INCLUDE [ssNoVersion](../../includes/ssnoversion-md.md)] Replication. |
 | [Install Distributed Replay](../../tools/distributed-replay/install-distributed-replay.md)<sup>1</sup> | Lists articles to install the Distributed Replay feature. |
 | [SQL Server Management Tools](/ssms/install/install) | Install and configure [!INCLUDE [ssNoVersion](../../includes/ssnoversion-md.md)] management tools. |