Microsoft SQL Server 2025, now in preview, is an enterprise AI-ready database from ground to cloud that tackles these challenges by bringing AI to customers’ data. This release continues SQL Server’s three decades of innovation in performance and security, adding new AI capabilities. With Microsoft Fabric integration, customers can bring their data into the next generation of data analytics. The release supports hybrid environments across clouds, on-premises datacenters, and edge, leveraging Microsoft Azure innovation for customers’ databases.

Over the years, SQL Server has transcended well beyond a traditional relational database. With the latest release of SQL Server, we’re enabling customers to build AI applications deeply integrated with the SQL engine. SQL Server 2025 is transforming into a vector database in its own right, using the built-in filtering capabilities along with a vector search, with great performance and is easily consumable by developers using T-SQL.

AI built-in

This new version has AI built-in, simplifying AI application development and retrieval-augmented generation (RAG) patterns with secure, performant, and easy-to-use vector support, leveraging familiar T-SQL syntax. With this new capability, you can combine vectors with your SQL data for a hybrid AI vector search.

Build AI applications with your enterprise database

SQL Server 2025 is an enterprise-ready vector database with built-in security and compliance, bringing enterprise AI to your data. It features a native vector store and index powered by DiskANN, a vector search technology using disk storage to efficiently find similar data points in large datasets. These databases efficiently support chunking and enable accurate data retrieval through semantic searching. In this latest SQL Server version, flexible AI model management within the engine using Representational State Transfer (REST) interfaces allows you to use AI models from ground to cloud.

In addition, whether customers are working on data preprocessing, model training, or RAG patterns, extensible, low-code tools offer flexible model interfaces within the SQL engine, supported by T-SQL and external REST endpoints. These tools enhance developers’ ability to create various AI applications through seamless integration with popular AI frameworks like LangChain, Semantic Kernel, and Entity Framework Core.

Boost developer productivity

When building data-intensive applications such as AI applications, it’s critical to focus on extensibility, frameworks, and data enrichment to enhance developers’ productivity. We ensure SQL will provide a best-in-class experience for developers by incorporating features such as REST API support, GraphQL integration through Data API Builder, and Regular Expression enablement. Additionally, native JSON support enables developers to more effectively deal with frequently changing schema and hierarchical data, facilitating the creation of more dynamic applications. Overall, we’re enhancing SQL development to be more extensible, performant, and user-friendly. All functionalities are underpinned by the security provided by the SQL Server engine, making it a truly enterprise-ready platform for AI.

Best-in-class security and performance

SQL Server 2025 is an industry leader in database security and performance. Support for Microsoft Entra managed identities improves credential management, reduces potential vulnerabilities, and provides compliance and auditing capabilities. SQL Server 2025 introduces outbound authentication support for MSI (Managed Service Identity) for SQL Server enabled by Azure Arc.

We’re also introducing performance and availability enhancements, extensively battle-tested on Microsoft Azure SQL, to SQL Server. In the new version you can boost workload performance and reduce troubleshooting with enhanced query optimization and query performance execution. Optional Parameter Plan Optimization (OPPO) is designed to enable SQL Server to choose the optimal execution plan based on customer-provided runtime parameter values and to significantly reduce bad parameter sniffing problems that can exist in workloads. Persisted statistics on secondary replicas prevent the loss of statistics during a restart or failover, thereby avoiding potential performance degradation. Regarding query execution, the improvements in batch mode processing and columnstore indexing further establish SQL Server as a mission-critical database for analytical workloads.   

Optimized locking reduces lock memory consumption and minimizes blocking for concurrent transactions through Transaction ID (TID) Locking and Lock After Qualification (LAQ). This capability enables customers to increase uptime and enhance concurrency and scale for SQL Server applications. 

Change event streaming for SQL Server brings real-time application integration with event driven architectures, command query responsibility segregation, and real-time intelligence. This will add new database engine capabilities to capture and publish incremental changes to data and schema to a provided destination such as Azure Event Hubs and Kafka in near real-time.

Microsoft Fabric and Azure Arc connected

In traditional data warehouse and data lake scenarios, integrating all your data involves designing, monitoring, and managing complex ETL (Extract, Transform, Load) processes to transfer operational data from SQL Server. These traditional methods do not support real-time data transfer, resulting in latency that prevents the creation of real-time analytics. Microsoft Fabric offers comprehensive, integrated, and AI-enhanced data analytics services designed to meet modern requirements of analytical workloads. Mirrored SQL Server Database in Fabric is a fully managed, resilient process that simplifies SQL Server data replication to Microsoft OneLake in near real-time. Mirroring will enable customers to continuously replicate data from SQL Server databases running on Azure virtual machines or outside of Azure, serving online transaction processing (OLTP) or operational store workloads directly into OneLake in order to facilitate analytics and insights on the unified Fabric data platform.

Azure continues to be a critical component of SQL Server. With Azure Arc, SQL Server 2025 will continue to offer cloud capabilities to enable customers better manage, secure, and govern their SQL estate at scale across on-premises and cloud. Capabilities like automatic patching, automatic backups, monitoring, and Best Practices Assessment offer customers more ways to streamline routine tasks and further enhance their business continuity. In addition, Azure Arc simplifies SQL Server licensing by offering a pay-as-you-go option, bringing flexibility and licensing visibility to our customers.

Sign up for the preview today

We’re currently onboarding customers and partners to SQL Server 2025 preview, in advance of general availability in the coming year. 

Register today to apply for the SQL Server 2025 Community Technology Preview (CTP)¹ and stay informed about SQL Server 2025 updates.

Microsoft just announced the upcoming release of SQL Server Management Studio (SSMS) 21 Preview 1. This release integrates Microsoft Copilot capabilities into SSMS. The Copilot experience streamlines SQL development by offering real-time suggestions, code completions, and best practice recommendations. If you would like to take part and have an early hands-on experience with this new capability, please use this link to indicate your interest.

¹Some of the new capabilities covered in this blog may not be available in the first CTP version.

The post Announcing Microsoft SQL Server 2025: Enterprise AI-ready database from ground to cloud appeared first on Microsoft SQL Server Blog.

Monitor SQL Server enabled by Azure Arc 

Simplifying deployment and cost management in multi-cloud environments.

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The traditional method of paying for SQL Server is buying license and software assurance. Now, you can connect your SQL Server to Azure Arc and pay only for the hours when your VM with SQL Server is online and your SQL instance is active. For the pricing information, see SQL Server 2022—Pricing | Microsoft.

While SQL Server 2022 integrates a pay-as-you-go billing option in the setup, the older versions require a product key to install any production edition. This blog provides instructions on how to install a pay-as-you-go image of any SQL Server version starting from SQL Server 2012 without purchasing a license and providing a product key.

The process in a nutshell

The deployment of a pay-as-you-go image on a virtual or physical machine running in any cloud, edge, or on-premises datacenters consists of three steps:

1. Installing the prerequisites on the target machines
2. Creating a support ticket with Microsoft Azure to request and download a pay-as-you-go image
3. Running the installation script

Prerequisites

Make sure your target machine meets the following requirements.

On target VMs:

• Windows Server instance is running, and you have a local administrator account.
• Azure PowerShell is installed and updated.
• For SQL Server version 2014, you will need to install .NET. For more information on this prerequisite, visit here.
• You have a remote desktop connection to connect to Windows Server as a local administrator.
• You copied the script from here to a local folder. Follow the download instructions in the readme file.
• Important: If your target VM runs Windows Server 2016, you must make sure that your Transport Layer Security (TLS) configuration is compatible with Azure. Follow the instructions in the readme file on how to mitigate the TLS version issue on Windows Server 2016.

On Microsoft Azure:

• You have an Azure account, subscription, and a target resource group.

Opening a support ticket

You can open the support ticket using a Support + Troubleshooting entry from the subscription which the newly installed SQL Server instance will be connected to. The following screenshots illustrate the flow. Make sure to use the answers as highlighted in the screenshots.

After you complete the support ticket creation process, Azure support will email you a private link to a workspace with the zip file you need to download.

Download the file to the VM where you want to install SQL Server, and unzip it to the same folder that contains the installation script.

Note: For SQL Server 2012 and SQL Server 2022 images, the keys are not required, and the .zip file will contain only the ISO file. For other SQL Server versions, the .zip file includes a product key and the ISO file. The script will recognize these differences.

Running the script

Follow the instructions in the readme file on how to launch the script. An Azure sign-in screen will prompt you to enter your Azure credentials or identify the account you’re already signed into. After this, the script proceeds unattended to carry out the following steps:

• Installing Azure PowerShell modules if they’re not already installed
• Logging into Azure with your assistance
• Onboarding the VM to Azure Arc
• Installing SQL Server on the Windows Server from the file you identified in the previous step
• Mounting the ISO file as a volume
• Installing SQL Arc Extension with a pay-as-you-go license type
• Displaying the status of the Azure resource for the connected SQL Server Instance

Disclaimer: The script has been tested on several combinations of VM products available on AWS and Google Cloud Platform with different versions of Windows Server and SQL Server, but it is not specific to these platforms, and you are welcome to try it on other clouds.

Supercharge SQL Server with Azure Arc

You can use this process to streamline the installation of pay-as-you-go images of SQL Server versions and editions of your choice to machines across different clouds, on-premises datacenters, and edge. By doing so, you can maintain control over the SQL Server compliance and optimize the cost based on the resource’s utilization.

The post Save money on Microsoft SQL Server licensing with Microsoft Azure Arc appeared first on Microsoft SQL Server Blog.

By migrating their databases to Azure, customers like Ernst and Young are modernizing their data estate and leveraging cutting-edge cloud innovations. However, the migration process can be complex, whether moving within the same database management system (homogeneous) or between different systems (heterogeneous). Microsoft offers a suite of tools for migration to simplify the migration process. To further enhance the user experience, we are streamlining the Azure database migration tools ecosystem. This involves retiring certain overlapping tools to simplify finding the right tool and provide unified migration experiences across all phases of migration. As part of this effort, effective 12/15/2024 we are replacing some tools with unified experiences that offer capabilities across various migration stages in the drive to modernize their data estate and take advantage of innovation in the cloud.

Azure Database Migration Guides

Step-by-step guidance for modernizing your data assets

Learn more

With a refined set of tools, you can confidently plan, assess, and execute your database migration with minimal downtime, ensuring a smooth transition to Azure SQL. Post the 12/15/24, retirement date, Microsoft will stop supporting these tools for any issues that arise and will not issue any bug fixes or further updates. Here is the list of tools that are planned for retirement and Microsoft recommended replacement tools.

Microsoft recommended tools for migration to Azure SQL

With the retirement of Database Migration Assistant for Oracle (DMAO), Database Schema Conversion Toolkit (DSCT), Data Access Migration Toolkit (DAMT), Database Experimentation Assistant (DEA), the Azure database migration tooling ecosystem is greatly simplified. Here is Microsoft’s recommendation for database migration tools for customers moving to Azure SQL. 

Homogenous migrations (SQL Server to Azure SQL) 

If the SQL Server that will be migrated is already enabled by Azure Arc, you can use Arc capabilities to perform a migration assessment and get optimal Azure SQL Target recommendations. Additionally, SQL Server enabled by Azure Arc provides multiple Azure benefits to SQL Servers outside Azure like automated backups and patching, Microsoft Defender for SQL, inventory of instances and databases, and Entra ID support. By enabling these Arc features, you can leverage cloud automation and security for Azure SQL Server even before you migrate. 

If the SQL Server outside Azure is not inventoried yet, you can use Azure Migrate for discovery, assessment and business case to know the right Azure SQL targets for your on-premises SQL Workloads and to get the projected cost savings of migrating to Azure SQL.

To migrate SQL Server into an Azure Virtual Machine with the same configuration as the source, users can use Azure Migrate to perform lift and shift migrations. SQL Server on Azure Virtual Machines allows you to easily migrate your SQL Server workloads to the cloud, offering SQL Server’s performance and security along with Azure’s flexibility and hybrid connectivity to address urgent business needs. Later you can evaluate one of the Azure SQL PaaS targets (Azure SQL Managed Instance, Azure SQL Database) and modernize to a PaaS service for better cost and workload performance optimizations. 

If you have completed an assessment and are ready to move to Azure SQL Managed Instance or Azure SQL Database, you can start your migration journey with Azure Migrate, you can use Azure Database Migration service or Azure SQL Migration extension for Azure Data Studio can be used. 

If the SQL Server estate is already inventoried, users can use Azure SQL Migration extension for Azure Data Studio to complete the entire migration journey i.e., perform assessment, get Azure SQL Target recommendations and perform migrations.

Heterogenous migrations (non-SQL Server databases to Azure SQL) 

With the availability of Target Assessment and SKU recommendation capabilities in SQL Server Migration Assistant (SSMA) along with existing code conversion and migration capabilities, SSMA becomes a single tool that you need to use to migrate from other source database platforms like Oracle, DB2, SAP ASE, MySQL, Access to Azure SQL or SQL Server. 

Learn more about modernizing your databases with Azure

• Get step-by-step guidance for modernizing your Oracle and SQL Server databases to Azure in the Azure Database Migration Guides.
• Read about the Azure Migrate Assessment for Azure SQL to get an overview of SQL Server assessment process using Azure Migrate.
• Know how to complete end to end SQL Server to Azure SQL migration journey with Azure SQL Migration extension for Azure Data Studio. 
• Find documentation for SQL Server Migration Assistant (SSMA), a tool for assessing and automating migration from Microsoft Access, DB2, MySQL, Oracle, and SAP ASE.

The post Modernize your database with the consolidation and retirement of Azure Database Migration tools appeared first on Microsoft SQL Server Blog.

“With our migration to PaaS, we got what we wanted: greater scalability, reliability, security, agility in managing our IT infrastructure—and greater peace of mind—all without the cost and hassle of doing this ourselves,” 

Daniel Engberg, Head of AI, Data, and Platforms at Scandinavian Airlines System  

Migrate to Microsoft Azure

Boost productivity and enable innovation.

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This blog post will guide you through several best practices our customers employed when faced with the SQL Server end-of-support moment. Customers have three choices for handling their out-of-support SQL Server workloads: moving or updating to Azure, upgrading to SQL Server 2022, or getting Extended Security Updates (ESUs) for additional preparation time. 

Migrate and modernize to Azure, a smooth path, a more powerful destination 

Migrating to a cloud platform is an essential step on the journey to modernization, and there are many choices. What makes SQL Server and Microsoft Azure SQL unique is that it’s built on the same engine, no matter where you deploy, which means you can build on your existing SQL experience while gaining the layered security, intelligent threat detection, and data encryption that Azure provides. 

Modernizing to Microsoft Azure SQL Managed Instance offers cost savings, scalability, security, seamless migration, productivity, and always up-to-date features. Some of the recent product highlights include Azure SQL Managed Instance Next-gen General Purpose, now in public preview, which supports twice as many Azure VMs configurations, making migration and modernization faster and easier than ever before for a larger number of customer scenarios. Customers can experience the full capabilities of managed SQL Server in the cloud at no cost for the initial 12 months with access to a General Purpose instance capable of accommodating up to 100 databases, along with 720 vCore hours of compute per month (non-accumulative) and 64 GB of storage through Azure SQL Managed Instance Free Tier, now in public preview. 

Modernizing your SQL Server workloads to Azure also presents a chance to utilize cutting-edge innovation like Microsoft Copilot. Microsoft Copilot in Azure has extended its capabilities to Microsoft Azure SQL Database with new skills designed to enhance the management and operation of SQL-based applications.  

Extending end-of-support time

If you are ready to move to the cloud but feel challenged to upgrade or modernize before the end of the support timeline, Extended Security Updates are available for free in Azure for SQL Server 2014 and 2012 and Windows Server 2012. Secure your workloads for up to three more years after the end of the support deadline by migrating applications and SQL Server databases to Microsoft Azure Virtual Machines. Free Extended Security Updates are available for Azure Virtual Machines including Microsoft Azure Dedicated Host, Microsoft Azure VMWare Solution, Nutanix Cloud Clusters on Azure, and Microsoft Azure Stack (Microsoft Azure Stack Hub, Microsoft Azure Stack Edge, and Microsoft Azure Stack HCI). Combining Extended Security Updates in Azure with Azure Hybrid Benefit further reduces your costs. With these pricing advantages, AWS is up to five times more expensive than Azure for SQL Server and Windows Server end-of-support workloads. 

In-place upgrade to SQL Server 2022 

Another way to stay protected is to upgrade your SQL Server to SQL Server 2022, the most Azure-enabled release yet. Get more out of your data with enhanced security, industry-leading performance and availability, and business continuity through Azure. 

SQL Server 2022 is the most Azure-enabled release of SQL Server, with continued innovation across performance, security, and availability. Gain deeper insights, predictions, and governance from your data at scale. Take advantage of enhanced performance and scalability with built-in query intelligence. 

Stay protected on-premises or in multi-cloud environments with Azure Arc 

Just as with SQL Server 2012, Extended Security Updates for SQL Server 2014 offers an enhanced cloud experience through Microsoft Azure Arc. First year coverage from Extended Security Updates started on July 10, 2024. With this more customer-centric approach, security updates will be natively available in the Microsoft Azure portal through Azure Arc. This also provides Azure benefits and flexible subscription billing for SQL Server 2014 workloads on-premises or in multi-cloud environments. 

We’re continuing to enhance the capabilities Azure Arc offers to Extended Security Updates. Just recently, physical-core licensing with unlimited virtualization was released for SQL Server 2012 and 2014 ESUs. For customers who need to maximize database performance or require security isolation and better resource management, physical core licensing provides a more cost-effective way to leverage Extended Security Updates via Azure Arc. 

Also, if you enabled ESU subscription in your production environment managed through Azure Arc, you can enable SQL Server ESU subscription in the non-production environment for free, through SQL Server Developer Edition or an Azure dev/test subscription. 

We encourage all our customers running SQL Server 2014, Windows Server 2012, and Windows Server 2012 R2 to start planning for the end of support. We have migration resources, best practices, and more, as well as a rich ecosystem of partners ready to help. To get started, please visit the following pages to learn more. 

Learn More 

• Why migrate Windows Server and SQL Server to Azure: ROI, innovation, and free offers 
• Get the full ROI and TCO reports from IDC: The Business Value of Microsoft Azure SQL Database and Azure SQL Managed Instance Workload and The Business Value of Microsoft Azure for SQL Server and Windows Server Workloads 
• Previous announcement on SQL Server 2014 End-of-Support moment: Protect SQL Server 2014 workloads with Azure flexibility – Microsoft SQL Server Blog

The post Modernize Microsoft SQL Server 2014 workloads with Azure appeared first on Microsoft SQL Server Blog.

During recent years, Azure has undergone significant evolution, marked by groundbreaking innovations like Microsoft Fabric and Azure Data Lake Storage. As we continue this journey, it remains imperative to keep evolving our approach on hybrid data storage, ensuring optimal empowerment for our SQL Server customers in leveraging the best from Azure.

Retirement of SQL Server Stretch Database 

On November 16, 2022, the SQL Server Stretch Database feature was deprecated from SQL Server 2022. For in-market versions of SQL Server 2019 and 2017, we had added an improvement that allowed the Stretch Database feature to stretch a table to an Azure SQL Database. Effective July 9, 2024, the supporting Azure service, known as SQL Server Stretch Database edition, is retired. Impacted versions of SQL Server include SQL Server 2022, 2019, 2017, and 2016.  

In July 2024, SQL Server Stretch Database will be discontinued for SQL Server 2022, 2019, 2017, and 2016. We understand that retiring an Azure service may impact your current workload and use of Stretch Database. Therefore, we kindly request that you either migrate to Azure or bring their data back from Azure to your on-premises version of SQL Server. Additionally, if you’re exploring alternatives for archiving data to cold and warm storage in the cloud, we’ve introduced significant new capabilities in SQL Server 2022, leveraging its data virtualization suite. 

The path forward 

SQL Server 2022 supports a concept named CREATE EXTERNAL TABLE AS SELECT (CETaS). It can help customers archive and store cold data to Azure Storage. The data will be stored in an open source file format named Parquet. It operates well with complex data in large volumes. With its performant data compression, it turns out to be one of the most cost-effective data storage solutions. Using OneLake shortcuts, customers then can leverage Microsoft Fabric to realize cloud-scale analytics on archived data.  

Our priority is to empower our SQL Server customers with the tools and services that leverage the latest and greatest from Azure. If you need assistance in exploring how Microsoft can best empower your hybrid data archiving needs, please contact us.

New solution FAQs

What’s CETaS? 

Creates an external table and then exports, in parallel, the results of a Transact-SQL SELECT statement. 

• Azure Synapse Analytics and Analytics Platform System support Hadoop or Azure Blob Storage.
• SQL Server 2022 (16.x) and later versions support CETaS to create an external table and then export, in parallel, the result of a Transact-SQL SELECT statement to Azure Data Lake Storage Gen2, Azure Storage Account v2, and S3-compatible object storage. 

What is Fabric? 

Fabric is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. Fabric offers a comprehensive suite of services including Data engineering, Data Factory, Data Science, Real-Time Analytics, Data Warehouse, and Databases. 

With Fabric, you don’t need to assemble different services from multiple vendors. Instead, it offers a seamlessly integrated, user-friendly platform that simplifies your analytics requirements. Operating on a software as a service (SaaS) model, Fabric brings simplicity and integration to your solutions. 

Fabric integrates separate components into a cohesive stack. Instead of relying on different databases or data warehouses, you can centralize data storage with Microsoft OneLake. AI capabilities are seamlessly embedded within Fabric, eliminating the need for manual integration. With Fabric, you can easily transition your raw data into actionable insights for business users. 

What is OneLake shortcuts?  

Shortcuts in OneLake allow you to unify your data across domains, clouds, and accounts by creating a single virtual data lake for your entire enterprise. All Fabric experiences and analytical engines can directly connect to your existing data sources such as Azure, Amazon Web Services (AWS), and OneLake through a unified namespace. OneLake manages all permissions and credentials, so you don’t need to separately configure each Fabric workload to connect to each data source. Additionally, you can use shortcuts to eliminate edge copies of data and reduce process latency associated with data copies and staging. 

Shortcuts are objects in OneLake that point to other storage locations. The location can be internal or external to OneLake. The location that a shortcut points to is known as the target path of the shortcut. The location where the shortcut appears is known as the shortcut path. Shortcuts appear as folders in OneLake and any workload or service that has access to OneLake can use them. Shortcuts behave like symbolic links. They’re an independent object from the target. If you delete a shortcut, the target remains unaffected. If you move, rename, or delete a target path, the shortcut can break. 

Learn more 

• Stretch Database
• Deprecated database engine features in SQL Server 2022
• Install an on-premises data gateway
• SQL Server in Microsoft Fabric? Use CETaS to move data!
• OneLake shortcuts
• CREATE EXTERNAL TABLE AS SELECT (CETaS)

Microsoft Fabric

Bring your data into the era of AI

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The post Announcing the retirement of SQL Server Stretch Database appeared first on Microsoft SQL Server Blog.

Azure SQL and SQL Server support building new generative AI experiences that become supercharged when combined with your data. In addition, SQL brings AI assistance to a new level with copilot experiences for both self-help and natural language to SQL capabilities.

In this blog post, I’ll share how you can get started with these new AI experiences—Azure SQL and SQL Server. First, check out our latest story on Microsoft Mechanics:

Responsible AI

Many conversations about AI starts with a statement on responsible AI. Microsoft has established a set of policies, research, engineering efforts, and principles to ensure AI technologies are adopted, implemented, and used in a responsible manner.

These principles include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Your data is your data. One promise for Microsoft is that private data of any user, including prompts and responses, are never used to fine tune a model that Microsoft hosts or implements.

Generative AI applications with your data

One of the motivations for generative AI applications is to become more productive, creative, and efficient through the generation of content in all forms: text, audio, and video. Many of today’s examples for generative AI applications involve the user of a natural language prompt and the interaction with a language model. Many of you have probably at some point used an application like ChatGPT or Microsoft Copilot which are great examples of generative AI applications.

Get smarter with your data

While these are great applications, they don’t know about your data. The combination of a generative AI application with your data, for example, stored in a database, can be quite powerful. Generative AI provides methods for smarter searching on your data. A common application pattern is to use language models with a prompt application to “chat with your data.” Using the concept of vector embeddings, language models allow you to get more precision on questions about your data. In addition, responses to questions are more tailored to your users and searches can often be faster because language models allow you to use the power of natural language. Generative AI applications with your data provide unique intelligence in an interactive manner, including conversations. Language models are trained to provide more context on your search, often giving you more (hence generated) content than you might normally get using common searching techniques within a database engine with a language like SQL.

Prompt engineering, Retrieval Augmented Generation fundamentals, and hybrid search

As you investigate how you can take advantage of generative AI with language models, there are two important concepts to understand:

Prompt engineering is the discipline of using high quality and descriptive prompts when interacting with a language model. The concept is simple. The better the prompt, the likelihood of a better response from the model. For example, let’s say you use Microsoft Copilot and type in a prompt like “What are the best steak restaurants in Fort Worth, Texas?” You will get a good list of steak houses in Fort Worth, Texas based on a search by Copilot of rankings across a broad set of searches. But what if you are on a bit of a tight budget? Instead of looking at the results from the prompt and trying to figure out what prices you can afford you could instead ask “I’m on a tight budget but want to eat at a good steakhouse in Fort Worth, Texas.” Now your results are more tailored for what you really want. And since you are interacting with a language model, it understands the phrase “tight budget” means you need choices that are good but affordable.

While this technique can be great if you are interacting with a model that is trained to help you search the internet, what about your own data? One prompt engineering technique to get smarter with your data is called Retrieval Augmented Generation (RAG). The concept of RAG is to search for information from a source of data and use those results to augment the prompt to the model. For Azure SQL and SQL Server, this could mean using standard SQL techniques to search for data using Transact-SQL (T-SQL), taking these results, and sending them along with the original prompt to the language model. This technique is simple and can be an effective way to get smarter with your data, and this can work with almost any type of data you search, not just SQL databases.

For Azure SQL and SQL Server, a more sophisticated technique is called hybrid search. With hybrid search, you can use the power of vector search combined with the query capabilities of your SQL data. Vector embeddings are numerical representations of data that capture semantic meaning and similarities. The key to embeddings with language models is that the model can generate embeddings based on data like text. This means you can take text data inside your SQL database and use a model to generate embeddings and then store these embeddings in your database. Now anytime you want to search for data inside the database, you can send a prompt to a language model which will generate embeddings for the prompt. And then you can use vector search techniques to compare the embeddings from the prompt with the embeddings stored in your database. You can then combine the vector search with other techniques you would normally use in T-SQL to find data in your database: a hybrid search.

There are methods today to use hybrid search completely inside the engine using T-SQL and outside the engine using Microsoft Azure AI Services or frameworks like LangChain or Semantic Kernel.

Get started quickly with Azure AI Services

One approach to get started quickly with no code required is to index your SQL database using Azure AI Search and then use Azure OpenAI Service to build a simple prompt app and “chat with your data” using a hybrid search technique.

You can use Azure AI Search to build an index based on a table in your SQL Server or Azure SQL database. When you build the index, you can apply a skillset to generate embeddings based on your data and store the result in the index. Now you can use Azure OpenAI with a prompt application to perform hybrid searches on your data. One example prompt application to perform simple testing is to use Azure AI Studio. In addition, as you change your SQL data, the index is automatically updated including the embeddings. The figure below shows the basic flow:

You can see this in action from the latest Microsoft Mechanics video or download a deck with demo recordings. One of the interesting aspects of this example is the method of changing the system message to direct the language model to respond in a unique way using the same data. This is also a great example of prompt engineering.

Learn more about Azure SQL in Azure AI Search.

Use hybrid search inside the engine with T-SQL

Let’s say instead of using a separate index, you would like to build generative AI capabilities for your application all inside the engine using T-SQL. You can do this in a very powerful way for Azure SQL Database today using a combination of vector embeddings, vector search, and other T-SQL search methods. This is a true hybrid search because you are using all the power of the SQL query processor together with a vector search. An example my colleague Davide Mauri has developed uses these techniques to help him find the best restaurant for one of this favorite Italian foods, focaccia bread.

Davide built an application that stores reviews from restaurants in the form of vector embeddings using Azure OpenAI Service with Azure SQL Database Representational State Transfer (REST) API inside the engine. With this in place, he can take any prompt to search for the best focaccia bread and use the same technique to generate embeddings for the prompt. Then, he can use a new T-SQL vector_distance function to perform a similarity search. The true power of SQL is possible because Davide built queries to combine this vector search with other criteria from spatial types, the new JSON data type, and the new Regular Expression (RegEx) T-SQL capabilities.

You can see a diagram of how these techniques are combined together below:

You can see this demo in action in our Microsoft Mechanics video or download a deck with demo recordings. You can learn more about the new JSON data type (preview). You can also sign-up to preview the new vector search capabilities and RegEx in Azure SQL Database.

Building generative AI applications using frameworks

There are other methods to build generative AI applications with Azure SQL and SQL Server using frameworks such as:

• LangChain:
  LangChain is an open-source framework to orchestrate AI applications with language models. You can use programming languages such as Python and JavaScript to build your own generative AI application. LangChain supports the SQL Agent Toolkit which allows you to interact with a SQL database using natural language prompts. The toolkit integrates the connection to your database with a language model to generate SQL queries based on natural language prompts. You can see an example of this in the blog post “Building your own DB Copilot for Azure SQL with Azure OpenAI GPT-4.”
• Semantic Kernel:
  Semantic Kernel is an open-source SDK to allow you to build AI applications in C#, Python, and Java, interfacing with many common models in the industry such as OpenAI, Azure OpenAI, and Hugging Face. A library has been built to allow a Semantic Kernel application to interact with Azure SQL Database (and use the new vector search capability) called the SQL Connector.

See a full range of SQL and generative AI examples.

The age of copilots

Microsoft has transformed the industry and how we work and live with a new set of AI assisted experiences called Microsoft Copilot. Copilots are AI companions that work everywhere you do and intelligently adapt to your needs.

Use Copilots where you live

I realize there seem to be copilots everywhere. It is hard to keep track. Microsoft is investing in Copilot experiences in almost every product or service. Use the product or service you normally do and see what Copilot can offer. For example, if you have Microsoft 365, use Copilot for Microsoft 365 naturally within Microsoft Teams or any Office product or service. I personally use Microsoft Copilot in my Edge browser or on the app on my phone for any search experience I need today—web or work related.

Microsoft Copilot in Azure

The primary resource to manage and explore Microsoft Azure is the Azure portal. You can now use Microsoft Copilot in Azure within the Azure portal to manage, deploy, and troubleshoot Azure resources. Azure SQL Database is one of the most popular Azure resources in the world, so we have built two distinct experiences within the Copilot in Azure framework using natural language for self-guided assistance and T-SQL query authoring:

Microsoft Copilot in Azure integration

One of the strengths of SQL Server is the deep built-in telemetry within the engine all accessible through T-SQL. This includes Dynamic Management Views (DMV) and Query Store. These rich, traditional capabilities shine through now in Copilot. For example, you can prompt with Copilot a general statement like “My database is slow” and Copilot, based on your permissions, will access real-time diagnostic data, in the context of your database, to help you quickly navigate difficult, and often vague, performance problems. Here is an example:

You can then continue a conversation with Copilot to tune the query causing the problem. There are many different skills that Copilot can help you all in the context of your database. Learn about all the possibilities of Copilot skills in Azure SQL Database (preview).

Natural language to SQL

The T-SQL query language has so many great capabilities and possibilities. But the open nature of T-SQL also leads to difficulties in crafting queries to meet the need of your application. Along comes a copilot experience to allow you to “chat” with your database using natural language in the context of your database and schema: table, columns, and key relationships. A simple example is being able use a natural language statement to generate a query that typically requires several joins over multiple tables like the following:

Learn more how to use natural language to SQL.

You can see both experiences in action in our Microsoft Mechanics video or download a deck with demo recordings.

Innovations moving forward

We are just beginning with SQL and AI. We have innovations for the future planned for enhancements with AI services, enhancements for deep integration for vector search, and enhanced Copilot experiences for SQL Server. Stay tuned for future blog posts showing all of these innovations.

Learn more today

Here are more resources for you to learn more about SQL and AI:

• Explore the capabilities of Azure SQL Database.
• Keep track of SQL and AI at our one-stop documentation hub.
• Keep track of all of our SQL and AI examples.
• Showcase your own demos using our decks with demo recordings built-in.
• Read our team’s blog on developer experiences for SQL including the latest AI examples at Azure SQL Devs’ Corner.
• To see all this in action, check out our new Microsoft Mechanics video.
• Learn the essentials to building a SQL generative AI application.
• Check out the comprehensive blog post from Sanjay Mishra to explore the power of generative AI applications with Azure SQL Database.
• Learn more about Copilot in Azure SQL Database experiences from Joe Sack’s Data Exposed episode.
• Check out our Microsoft Build 2024 sessions:
  • Power AI apps and develop rich experiences with Azure SQL Database
  • The power of AI and Copilot for Azure Databases

Azure SQL

Migrate, modernize, and innovate with the modern SQL family of cloud database services

Start your journey

The post Getting started with delivering generative AI capabilities in SQL Server and Azure SQL appeared first on Microsoft SQL Server Blog.

We’ve been on the road the last couple of weeks at MVP Summit, SQLBits and Fabric Con, connecting with customers talking about the value of migration and modernization. We want to dig into specifically, how Azure can deliver real business value through cost optimization and streamlined productivity for their Windows Server and SQL Server deployments when they migrate to Azure. 

We’ve helped countless organizations migrate their SQL Server and Windows workloads to Azure a critical 1^st step in any transformation initiative. The move can help improve cybersecurity posture and business continuity, boost productivity, and lay the foundation for AI and other highly scalable data innovations, while automating updates, backups, and other time-consuming IT tasks. 

Modernize and lower total cost of ownership (TCO) 

Migration is a business strategy that pays off. In The Business Value of Microsoft Azure SQL Database and Azure SQL Managed Instance Workload,¹ organizations that migrated to Azure SQL Managed Instance and Microsoft Azure SQL Database can get up to 406 percent return on investment over 3 years and can expect a 30-percent reduction in TCO over 5 years, protecting an additional $6.85 million in annual revenue.

A separate study found that customers that migrated both Windows Server and SQL Server workloads to Azure generated more value. According to The Business Value of Microsoft Azure for SQL Server and Windows Server Workloads,² by optimizing costs, operations, and business opportunities, companies gained $15.85 million in total annual benefits while also increasing IT security efficiency by 43 percent with cloud tools and automation.

Azure SQL

Migrate, modernize, and innovate with the modern SQL family of cloud database services.

Start today

A smooth path to migration, a more powerful destination

Migrating to a cloud platform is an essential step on the journey to modernization, and there are many choices. 

What makes SQL unique is that it’s built on the same engine, no matter where you deploy, which means you can build on your existing SQL experience while gaining the layered security, intelligent threat detection, and data encryption that Azure provides. And as we shared with customers at SQLBits, there’s now an even more powerful option available for customers looking to leverage the full PaaS experience. Azure SQL Managed Instance Next-gen GP  brings significantly improved performance and scalability to power up your existing Azure SQL Managed Instance fleet, and help bring more mission-critical SQL workloads to Azure. With close to 100 percent feature compatibility with SQL Server, Azure SQL Managed Instance is the recommended choice to migrate and modernize SQL apps at scale and at your own pace.

Another option many of our customers start with is by running their Windows Server workloads on Azure Virtual Machines, benefiting from a simplified, managed experience and cloud-native support for SQL Server, .NET apps, and Remote Desktop Services. Or you can modernize your entire Windows Server estate, choosing from more than 200 Azure services and capabilities, including support for hybrid environments. 

Take the first step or the next: You have choices

When it comes to migration, Azure meets you where you are with options for moving on-premises workloads and for developing new cloud solutions. For example, many organizations start by moving Windows Server workloads to Azure Virtual Machines, enabling them to easily scale to support new developments and more efficiently manage peak loads. Hokkoku Bank took this step, migrating its Windows Server–based estate to Azure as part of a cloud-first initiative. Azure supports the bank’s modernization plans and helps provide a disaster recovery solution in an earthquake-prone region.  

Correios de Portugal, the country’s postal service, migrated its Windows Server workloads to Azure Virtual Machines backed by Azure SQL, which provides a smooth path to a cost-effective, highly scalable, fully managed PaaS database. It’s the best choice for modernizing your apps and getting the most out of your existing investments.

Many of our database customers move to SQL Server on Azure Virtual Machines for the cost benefits on top of the scalability and resilience of Azure. As an example, healthcare software manufacturer Allscripts migrated on-premises applications to Azure SQL Database Managed Instance when possible, but another 600 on-premises VMs needed a different migration approach. Allscripts moved them to SQL Server on Azure Virtual Machines, a quick, low-risk step for workloads it plans to optimize and modernize later. The lift-and-shift approach can be an easy first   step in your cloud journey.

Azure also offers hybrid solutions that bridge your on-premises and cloud resources. For example, you can move or extend on-premises VMware environments using Azure VMWare Solution. You can even use the free Windows Admin Center tool to manage across Windows Server environments—physical, virtual, on-premises, in Azure, or in a hosted environment—at no additional cost. To get started with a Windows Server migration, start discovering and assessing on-premises resources using the free Azure Migrate tool.

Watch the Migrate to Innovate digital event on demand and learn the business benefits of migrating to Azure.

Try it for free 

If you want to know how your workload will perform before migrating, try these Azure offers and get started building that proof-of-concept.  

• Try Azure SQL Managed Instance for free. For 12 months, you can get up to two instances per Azure subscription, 750 vCore hours of compute per month, and 32 GB data storage and 32 GB backup storage per month. 

• Try Azure SQL Database for free. Test and develop applications or run small production workloads for free. This offer provides the first 100,000 vCore seconds, 32 GB of data, and 32 GB of backup storage per month at no charge for the lifetime of your subscription. 

• Try Windows Server in the cloud with an Azure free account. Extend your existing Windows Server datacenter and try out popular services free for 12 months. 

Learn more about Azure SQL

Stay tuned for more migration announcements in the coming months. To get started now: 

• Get the full ROI and TCO reports from IDC: The Business Value of Microsoft Azure SQL Database and Azure SQL Managed Instance Workload and The Business Value of Microsoft Azure for SQL Server and Windows Server Workloads.

• Review the benefits of migration in Better on Azure: The benefits of using Azure for Windows Server and SQL Server workloads. 

• Discover why cloud economics make sense and get greater return on your investment. 

1. IDC report, The Business Value of Microsoft Azure SQL Database and Azure SQL Managed Instance Workloads, IDC #US51073123, August 2023. 
2. The Business Value of Microsoft Azure for SQL Server and Windows Server Workloads

The post Why migrate Windows Server and SQL Server to Azure: ROI, innovation, and free offers appeared first on Microsoft SQL Server Blog.

We’re excited to announce the public preview of the Premium SSD v2 provisioning experience for SQL Server on Azure Virtual Machines (VMs) deployed in the Azure portal. Premium SSD v2 storage improves performance, reliability, and scalability of your SQL Server workloads while offering robust resource capacity, as you can create a single disk with up to 64 TiBs, 80,000 input/output per second (IOPS), and 1,200 MB/s throughput.

When building Azure SQL VMs in the cloud, DBAs have several storage choices they can consider to give their applications the required performance and capacity their workloads require. In Azure, DBAs have compute options along with affordable storage designed to handle mission critical SQL Server workloads. In this article, we’ll review these options focusing on the latest capabilities of Premium SSD v2 storage and the Ebds_v5 Azure VMs, which are better together—providing the best combination of price performance capabilities in the cloud for SQL Server workloads.

Premium SSD v2 storage in Azure portal

Deploy SQL Server on Azure VMs with Premium SSD v2 disks using the Azure portal.

Learn how

Managed disk storage options

DBAs looking to move their mission critical SQL Server applications from on-premises to the cloud have the managed disk options of Premium SSD (Gen 1), Premium SSD v2, and Ultra Disk for their production workloads where Standard SSDs are often used for dev/test and smaller departmental workloads. The goal of this article is to review the Azure VM storage options for SQL Server and give DBAs the tools and information needed to weigh the possible trade-offs in Azure feature availability and overall costs.

Premium SSD (Gen 1)

For most DBAs looking to build scalable storage with optimal price-performance, they use Premium SSD (Gen 1) managed disks for their storage solutions. Using first generation Premium SSD disks, DBAs can provision their VMs by striping disks of usually Premium SSD P30 or P40 disks in a storage pool. A storage pool allows DBAs to multiply disks up to the VM’s limits providing scalability and maximizing price-performance, while Premium SSD (Gen 1) disks allow DBAs to take advantage of Azure reservations and read-only host-caching.

While Premium SSD (Gen 1) storage exhibits remarkable flexibility and operates with minimal Azure feature limitations in the cloud, along with offering price-performance options through reservations for select disk types, it cannot scale to meet the demands of the latest Ebdsv5 VM series.

Additional differences between Premium SSD and Premium SSD v2 include:

• Premium SSD v2 offers higher disk capacity, up to 64 TiBs per disk, compared to 32 TiBs for Premium SSD.
• Premium SSD v2 provides more flexibility and control over disk performance, as you can independently adjust the disk size, IOPS, and throughput according to your workload needs, while Premium SSD (Gen 1) has fixed performance tiers based on disk size.
• Premium SSD v2 has lower latency and higher reliability than Premium SSD as it uses newer hardware and an improved storage platform.
• Premium SSD v2 supports higher levels of bursting compared to the previous generation, which allows you to achieve additional performance when needed for short periods of time without additional cost.

Ultra Disk

DBAs can also use Ultra Disk to meet the storage demands of the latest generation of Azure VMs as they offer sub-millisecond latency and better performance than Premium SSD (Gen 1). Like Premium SSD v2, Ultra Disk also allows DBAs to dynamically configure and scale the IOPS, throughput, and capacity of their disks independently without having to restart the VM or change the disk size. This makes Ultra Disk an attractive option for data-intensive workloads such as SQL Server that require consistency and high performance with low latency.

However, Ultra Disk also has drawbacks that make it less suitable for some scenarios. For example, Ultra Disk is only available in a limited number of regions and has stricter requirements for VM sizes, zones, and proximity placement groups. Ultra Disk also does not support disk snapshots, disk encryption, Microsoft Azure Site Recovery, or host caching options. Moreover, Ultra Disk is much more expensive than both Premium SSD and Premium SSD v2, especially for larger disk sizes.

Comparing VM and storage deployments

The established guidance for SQL Server VM deployments was to use Premium SSD (Gen 1) in a storage pool configuration with read-only caching for the data files. For the transaction log, we advised using Ultra Disk in cases where DBAs needed lower latency and could handle the limitations. This recommendation was especially the case for our previous hero VM series such as the Edsv4-series which offered the best performance for OLTP workloads at the time.

However, VMs continued to improve and with Azure Boost and other hardware enhancements, the newest Ebs_v5 and Ebds_v5 VMs have proven to be the optimal VM series for SQL Server workloads. The newest Ebs_v5 and Ebds_v5 VMs power higher levels of IOPS and throughput, and now with NVMe storage interface support they can scale well beyond the capabilities of Premium SSD (Gen 1). The Ebs_v5 and Ebds_v5 VMs series and larger VMs on the horizon will require a higher level of storage performance than Premium SSD (Gen 1) was able to provide. A higher level of storage performance is needed to match the capabilities of our newest generation of Azure SQL VMs and to avoid being throttled/capped when your application is pushing higher levels of IOPS/ throughput.

The next generation of Azure VMs will further push well beyond the largest storage needs of our current generation. For example, the largest machine in the previous generation Edsv4-series is the E80ids_v4 which is an Azure SQL VM of 80 vCores, 504 GiBs memory, 80,000 max uncached IOPS, and 1,500 MBps max uncached disk throughput. For a machine of this size, a Premium SSD storage pool would require 16 x P30 disks to provide the same number of IOPS that a single Premium SSD v2 disk could achieve, but with improved latency and less overall cost.

In comparison, the Ebds_v5 series has a VM size of E112ibds_v5 that supports 400,000 max uncached IOPS and 10,000 MBps max uncached disk throughput (Ultra/Pv2-SSD). Premium SSD (Gen 1) would require 80 disks in order to match the IOPs capabilities of this VM, which would exceed the max data disk limit of the VM.

Premium SSD v2 only needs five disks in a storage pool and additionally allows adjusting the IOPS and throughput based on needs for a better overall total cost of ownership (TCO).

Azure SQL VM best practices

Premium SSD v2 has more flexibility than Premium SSDs (Gen 1) and Ultra Disk. You can choose any supported size for a Premium SSD v2 and change the performance parameters without interruption. Premium SSD v2 does not have host caching, but it has much lower latency, which helps with some of the same issues that host caching helps with. The ability to modify IOPS, throughput, and size on demand means you can reduce the management workload of having to combine disks to meet your needs.

To get started, when provisioning a new SQL Server on Azure VM in the Azure portal, you can choose Premium SSD v2 for eligible VMs:

Premium SSD v2 allows you to change disk size, IOPS, and throughput independently to reach your performance targets, making workloads more cost efficient while also adapting to changing performance needs.

With a capacity of 64 TiBs, 80,000 IOPS, and 1,200 MBps of throughput, most environments can benefit from the performance capabilities of a single Premium SSD v2 disk—but for our largest Azure VMs, Premium SSD v2 disks can be combined into a storage pool to provide the performance required for a single logical drive.

When deploying your SQL Server VM image in the Azure portal, Premium SSD v2 is available for the Ebds_v5 and Ebs_v5 Azure VM series which are optimized for high-performance database workloads.

The following table helps visualize some of the performance gains and cost savings when using Premium SSD v2 with your Ebds_v5 and Ebs_v5 VMs:

Learn more about Premium SSD v2 storage for SQL Server on Azure VMs

In summary, Premium SSD v2 offers enhanced performance, granular scalability, and cost-effectiveness for applications demanding sub-millisecond disk response times. While it provides more capabilities, the actual cost difference between Premium SSD, Premium SSD v2, and Ultra Disks depends on factors such as region, disk size, IOPS, and throughput. You can use the Azure pricing calculator to estimate costs based on your specific needs.

If you are deploying SQL Server VMs using the Azure portal and want to utilize Premium SSD v2, note that it is currently limited to the Ebds_v5 or Ebs_v5 series VMs in this public preview phase.

We’re committed to providing our customers with the best possible experience when running SQL Server on Azure VMs. The addition of Premium SSD v2 storage is another step toward that goal.

Try out Premium SSD v2 storage for SQL Server on Azure VMs today and please share your feedback with us. We look forward to hearing from you as we continue to improve our offerings for SQL Server on Azure VMs.

To get started, check out Use Premium SSDv2 storage with your SQL Server on Azure VMs.

You can also keep an eye on the What’s new page for all the latest and greatest updates to SQL Server on Azure VMs and What’s new for Azure storage.

The post Provision Premium SSD v2 Storage for Microsoft SQL Server on Azure Virtual Machines in the Microsoft Azure portal appeared first on Microsoft SQL Server Blog.

To generate a production-ready schema-only copy of a database, it’s highly recommended to utilize tools such as Microsoft SQL Server Data Tools (SSDT); the Generate and Publish scripts Wizard; or data-tier application extraction, which includes either the Extract Data-tier Application (DAC) Wizard or a PowerShell script. These tools provide a reliable way to create a copy of a database for use in production environments.

Learn more about

• DBCC CLONEDATABASE (Transact-SQL) – SQL Server
• Clone an Existing Database – SQL Server Data Tools (SSDT)
• Generate and Publish Scripts Wizard – SQL Server Management Studio (SSMS)
• Create a statistics-only database using a generated statistics script – SQL Server
• Extract a DAC from a Database – SQL Server

Frequently asked questions

What is SSDT?

SQL Server Data Tools (SSDT) is a modern development tool that integrates with Microsoft Visual Studio and provides design, debugging, and deployment capabilities for building SQL Server relational databases, databases in Azure SQL, Analysis Services (AS) data models, Integration Services (IS) packages, and Reporting Services (RS) reports. With SSDT, developers can perform necessary tasks without needing the admin-focused SQL Server Management tool on their developer computer. Essentially, Visual Studio removes unnecessary features like admin tools, and focuses on tools that are useful for developers, like database and schema comparison tools.

What is the Generate and Publish Scripts Wizard?

The Generate and Publish Scripts Wizard is a feature in SQL Server Management Studio (SSMS) that allows you to create scripts for transferring a database between instances of the SQL Server Database Engine or Azure SQL Database. You can generate scripts for a database on an instance of the Database Engine in your local network, or from SQL Database. The generated scripts can be run on another instance of the Database Engine or SQL Database. You can also use the wizard to publish the contents of a database directly to a Web service created by using the Database Publishing Services. You can create scripts for an entire database or limit it to specific objects.

What is a data-tier application (DAC)?

A data-tier application (DAC) is a logical database entity that defines all of the SQL Server objects—such as tables, views, and instance objects, including logins—associated with a user’s database. A DAC is a self-contained unit of the entire database model and is portable in an artifact known as a DAC package, or .dacpac. Tooling support for data-tier applications enables developers and database administrators to deploy dacpacs to new or existing databases. Deployments to an existing database update the database model from the existing state to match the contents of the dacpac. Developers build DACs from SQL database projects, a declarative development concept for building SQL objects that enables source control on the database schema.

A .bacpac is a related artifact that, by default, encapsulates the database schema and the data stored in the database. The primary use case for a BACPAC is to move a database from one server to another—or to migrate a database from a local server to the cloud—and archiving an existing database in an open format.

What is DBCC CLONEDATABASE command?

DBCC CLONEDATABASE creates a new database that contains the schema of all the objects and statistics from the specified source database. Cloned databases copy all schema and metadata of the source database without copying any data.

How do I use Schema Compare to compare different database definitions?

SSDT includes a Schema Compare utility that you can use to compare two database definitions. The source and target of the comparison can be any combination of connected database, SQL Server database project or snapshot, or .dacpac file. The results of the comparison appear as a set of actions that must be taken with the target to make it the same as the source. Once the comparison is complete, you can update the target directly (if the target is a project or a database) or generate an update script that has the same effect.

The differences between source and target appear in a grid for easy review. You can drill into and review each difference in the results grid or in script form. You can then selectively exclude specific differences.

You can save comparisons either as part of an SQL Server Database project or as a standalone file. You can also set options that control the scope of the comparison and aspects of the update. Then you can save the comparison so that you can easily repeat the same comparison later or use it as the starting point for a new comparison.

Why do I need to generate a schema-only clone of a database with statistics in SQL Server?

You will need to generate a schema-only clone of a database with statistics to investigate performance issues.

The query optimizer in SQL Server uses the following types of information to determine an optimal query plan:

• Database metadata
• Hardware environment
• Database session state

Typically, you must simulate all these same types of information to reproduce the behavior of the query optimizer on a test system.

Microsoft Customer Support Services might ask you to generate a schema script of the database along with statistics to investigate a query optimizer issue.

The post Update on the support of DBCC CLONEDATABASE for production use appeared first on Microsoft SQL Server Blog.

This week, we are proud to be back in front of our community at SQLBits, together with our partner AMD to share the latest innovations from ground to cloud and beyond. From SQL Server, Azure SQL, to our powerful solutions like Microsoft Fabric and Microsoft Copilot, these product enhancements are designed to help make application migration, modernization, and development easier so you can power what’s next.

Better performance with the next generation of Azure SQL Managed Instance ​ 

What sets the SQL Server family apart from other operational databases is that it’s all built on the same SQL engine. So, whether you’re running at the edge or in the cloud, the power to unlock the potential of data and AI is always there. We’re announcing the public preview of Azure SQL Managed Instance Next-gen GP, now even more powerful and performant. Accelerate your migrations and efficiently manage your unique workload demands. Enhance your productivity with superior performance and adaptable compute and storage choices. Join the preview to maximize your efficiency.  

We now have a way customers can get stated with Azure SQL Managed Instance. Our free offer gives you:

• A General Purpose instance with up to 100 databases
• 720 vCore hours of compute every month
• 64 GB of storage

Azure SQL Managed Instance

Enhance your productivity with superior performance and more

Try it now free

AI-power your Azure SQL Database experience with Copilot 

We are bringing the power of Copilot to Azure SQL Database, now in private preview. Copilot in Azure SQL Databases delivers a set of AI-enhanced experiences built to help streamline design, operation, optimization of Azure SQL Database-driven applications, and improve productivity in the Azure Portal. This new functionality introduces two new Azure portal experiences: 

• Natural language to SQL: This experience within the Azure portal query editor for Azure SQL Database translates natural language queries into SQL, making database interactions more intuitive.  
• Microsoft Copilot for Azure integration: This experience adds Azure SQL Database skills into Copilot for Azure, customers with self-guided assistance, empowering them to manage their databases and solve issues independently.  

Sign up for the preview access.

Simplify your journey to Azure with SQL Server enabled by Azure Arc 

Earlier this month, we introduced a way to streamline migration to Azure SQL with SQL Server enabled by Azure Arc. Migration assessment removes some of the complexity around cloud migration by helping you better assess your SQL Server readiness for Azure SQL. Through the Azure Arc agent, customers can get help with:

• Streamlining discovery and migration readiness assessments.
• Evaluating and measuring the readiness of SQL Server instance and databases.
• Getting best-fit recommendations.   

Learn about this assessment “SQL Server enabled by Azure Arc, now assists in selecting the best Azure SQL target.” 

Deliver better value and power AI with Flexible Server in Azure Database for PostgreSQL 

In November 2023, we announced the preview of the new Azure AI extension, enabling you to integrate Azure AI services with your operational data in Azure Database for PostgreSQL. Now, we’re sharing that Flexible Server in Azure Database for PostgreSQL is now directly integrated with Azure OpenAI Service. Learn how to use Azure AI with Azure Database for PostgreSQL.

We were excited to have our AMD partners join us to co-sponsor SQLBits to showcase how best in class technology partnerships can help customers achieve their business outcomes for both SQL Server and PostgreSQL workloads. A recently commissioned Principled Technologies report found that for customers who migrated to Azure Databases for PostgreSQL—flexible server, backed by AMD EPYC™ processors—saw significantly faster online transaction processing (OLTP) performance, in fact, 4.71 times new orders per minute when compared to single server. Customers also achieved better value, 3.88 times the performance per dollar. Read the full report.

We look forward to the week ahead and connecting with you in person. 

The post Expand the limits of innovation with Azure data appeared first on Microsoft SQL Server Blog.