Microsoft.Data.SqlClient 7.0 Is Here: A Leaner, More Modular Driver for SQL Server
Today we're shipping the general availability release of Microsoft.Data.SqlClient 7.0, a major milestone for the .NET data provider for SQL Server. This release tackles the single most requested change in the repository's history, introduces powerful new extensibility points for authentication, and adds protocol-level features for Azure SQL Hyperscale, all while laying the groundwork for a more modular driver architecture. If you take away one thing from this post: the core SqlClient package is dramatically lighter now. Azure dependencies have been extracted into a separate package, and you only pull them in if you need them. dotnet add package Microsoft.Data.SqlClient --version 7.0.0 The #1 Request: A Lighter Package For years, the most upvoted issue in the SqlClient repository asked the same question: "Why does my console app that just talks to SQL Server pull in Azure.Identity, MSAL, and WebView2?" With 7.0, it doesn't anymore. We've extracted all Azure / Microsoft Entra authentication functionality into a new Microsoft.Data.SqlClient.Extensions.Azure package. The core driver no longer carries Azure.Core, Azure.Identity, Microsoft.Identity.Client, or any of their transitive dependencies. If you connect with SQL authentication or Windows integrated auth, your bin folder just got dramatically smaller. For teams that do use Entra authentication, the migration is straightforward. Add one package reference and you're done: dotnet add package Microsoft.Data.SqlClient.Extensions.Azure No code changes. No configuration changes. You can also now update Azure dependency versions on your own schedule, independent of driver releases. This is something library authors and enterprise teams have been asking for. Pluggable Authentication with SspiContextProvider Integrated authentication in containers and cross-domain environments has always been a pain point. Kerberos ticket management, sidecar processes, domain trust configuration: the workarounds were never simple. Version 7.0 introduces a new public SspiContextProvider API on SqlConnection that lets you take control of the authentication handshake. You provide the token exchange logic; the driver handles everything else. var connection = new SqlConnection(connectionString); connection.SspiContextProvider = new MyKerberosProvider(); connection.Open(); This opens the door to scenarios the driver never natively supported: authenticating across untrusted domains, using NTLM with explicit credentials, or implementing custom Kerberos negotiation in Kubernetes pods. A sample implementation is available in the repository. Async Read Performance: Packet Multiplexing (Preview) One of the most community-driven features in 7.0 is packet multiplexing, a change to how the driver processes TDS packets during asynchronous reads. Originally contributed by community member Wraith2, this work delivers a significant leap in async read performance for large result sets. Packet multiplexing was first introduced in 6.1 and has been refined across the 7.0 preview cycle with additional bug fixes and stability improvements. In 7.0, it ships behind two opt-in feature switches so we can gather broader real-world feedback before making it the default: AppContext.SetSwitch("Switch.Microsoft.Data.SqlClient.UseCompatibilityAsyncBehaviour", false); AppContext.SetSwitch("Switch.Microsoft.Data.SqlClient.UseCompatibilityProcessSni", false); Setting both switches to false enables the new async processing path. By default, the driver uses the existing (compatible) behavior. We need your help. If your application performs large async reads (ExecuteReaderAsync with big result sets, streaming scenarios, or bulk data retrieval), please try enabling these switches and let us know how it performs in your environment. File your results on GitHub Issues to help us move this toward on-by-default in a future release. Enhanced Routing for Azure SQL Azure SQL environments with named read replicas and gateway-based load balancing can now take advantage of enhanced routing, a TDS protocol feature that lets the server redirect connections to a specific server and database during login. This is entirely transparent to your application. No connection string changes, no code changes. The driver negotiates the capability automatically when the server supports it. .NET 10 Ready SqlClient 7.0 compiles and tests against the .NET 10 SDK, so you're ready for the next major .NET release on day one. Combined with continued support for .NET 8, .NET 9, .NET Framework 4.6.2+, and .NET Standard 2.0 (restored in 6.1), the driver covers the full spectrum of active .NET runtimes. ActiveDirectoryPassword Is Deprecated: Plan Your Migration As Microsoft moves toward mandatory multifactor authentication across its services, we've deprecated SqlAuthenticationMethod.ActiveDirectoryPassword (the ROPC flow). The method still works in 7.0, but it's marked [Obsolete] and will generate compiler warnings. Now is the time to move to a stronger alternative: Scenario Recommended Authentication Interactive / desktop apps Active Directory Interactive Service-to-service Active Directory Service Principal Azure-hosted workloads Active Directory Managed Identity Developer / CI environments Active Directory Default Quality of Life Improvements Beyond the headline features, 7.0 includes a collection of improvements that make the driver more reliable and easier to work with in production. Better retry logic. The new SqlConfigurableRetryFactory.BaselineTransientErrors property exposes the built-in transient error codes, so you can extend the default list with your own application-specific codes instead of copy-pasting error numbers from source. More app context switches. You can now set MultiSubnetFailover=true globally, ignore server-provided failover partners in Basic Availability Groups, and control async multi-packet behavior, all without modifying connection strings. Better diagnostics on .NET Framework. SqlClientDiagnosticListener is now enabled for SqlCommand on .NET Framework, closing a long-standing observability gap. Connection performance fix. A regression where SPN generation was unnecessarily triggered for SQL authentication connections on the native SNI path has been resolved. Performance improvements. Allocation reductions across Always Encrypted scenarios, SqlStatistics timing, and key store providers. Upgrading from 6.x For most applications, upgrading is a package version bump: dotnet add package Microsoft.Data.SqlClient --version 7.0.0 If you use Microsoft Entra authentication, also add: dotnet add package Microsoft.Data.SqlClient.Extensions.Azure If you use ActiveDirectoryPassword, you'll see a compiler warning. Start planning your migration to a supported auth method. Review the full release notes in release-notes/7.0 for the complete list of changes across all preview releases. Thank You to Our Contributors Open-source contributions are central to SqlClient's development. We'd like to recognize the community members who contributed to the 7.0 release: edwardneal · ErikEJ · MatthiasHuygelen · ShreyaLaxminarayan · tetolv · twsouthwick · Wraith2 What's Next We're continuing to invest in performance, modularity, and modern .NET alignment. Stay tuned for updates on the roadmap, and keep the feedback coming. Your issues and discussions directly shape what we build. NuGet: Microsoft.Data.SqlClient 7.0.0 GitHub: dotnet/SqlClient Issues & Feedback: github.com/dotnet/SqlClient/issues Docs: Microsoft.Data.SqlClient on Microsoft LearnUnlocking Enterprise AI: SQL Server 2025 and NVIDIA Nemotron RAG Accelerate AI
Today, most of the world’s data still remains untapped, sitting in databases, documents, and systems across organizations. Enterprises are racing to unlock this data’s value by building the next wave of generative AI applications—solutions that can answer questions, summarize documents, and drive smarter decisions. At the heart of these innovations are retrieval-augmented generation (RAG) pipelines, which enable users to interactively engage with large amount of data that continuously evolves. Yet, as promising as RAG pipelines are, enterprises face real challenges in making them work at scale. Handling both structured and unstructured data, processing massive volumes efficiently, and ensuring privacy and security are just a few hurdles. This is where the integration between SQL Server 2025 and NVIDIA Nemotron RAG models, deployed as NVIDIA NIM microservices, comes in, offering a new approach that streamlines AI deployment and delivers enterprise-grade performance—whether you’re running workloads in the cloud or on-premises. “As AI becomes core to every enterprise, organizations need efficient and compliant ways to bring intelligence to their data,” said Joey Conway, Senior Director of Generative AI software at NVIDIA. “With SQL Server 2025’s built-in AI and NVIDIA Nemotron RAG, deployed as NIM microservices, enterprises can deploy and run AI models close to their data on premises or in the cloud without complex integration, accelerating innovation while maintaining data sovereignty and control.” Overcoming the complexity of generating embeddings at scale Customer challenge Building responsive AI applications using RAG requires converting SQL data into vector embeddings—a process that feeds huge amounts of text through complex neural networks. This is inherently parallel and compute-intensive, often creating performance bottlenecks that prevent real-time data indexing. The result? Slow applications and poor user experiences. Moreover, enterprises need flexibility. Different embedding models excel at different tasks—semantic search, recommendations, classification—and each comes with its own tradeoffs in accuracy, speed, and cost. Businesses want to mix and match models, balance premium performance with budget constraints, and stay resilient against model deprecation or API changes. Furthermore, rapid experimentation and adaptation are key to staying ahead and thus developers want models that offer flexible customization and full transparency. The Solution: SQL Server 2025 + NVIDIA Nemotron RAG SQL Server 2025 brings AI closer to your data, allowing you to natively and securely connect to any model hosted anywhere. You can generate embeddings directly in SQL using extensions to T-SQL —no need for new languages, frameworks, or third-party tools. By connecting SQL Server 2025 to the llama-nemotron-embed-1b-v2 embedding model from NVIDIA, you eliminate bottlenecks and deliver the massive throughput needed for real-time embedding generation. llama-nemotron-embed-1b-v2 is a best in class embedding model that offers multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage. This model is part of NVIDIA Nemotron RAG models, a collection of extraction, embedding, reranking models, fine-tuned with the Nemotron RAG datasets and scripts, to achieve the best accuracy. These models offer flexible customization, enabling easy fine-tuning and rapid experimentation. They also offer full transparency with open access to models, datasets, and scripts. Llama-nemotron-embed-1b-v2 is the model of choice for embedding workflows, but this high-speed inference pipeline is not limited to this model and can potentially call any optimized AI model as an NVIDIA NIM microservice, seamlessly powering every stage of the RAG pipeline. From multimodal data ingestion and advanced retrieval to reranking, all operations run directly on your data within SQL Server. Such RAG systems can be applied across a wide range of use cases, enabling intelligent, context-aware applications across industries. Customer Benefits: With GPU acceleration and built-in AI of SQL Server 2025, you can achieve optimal inference, ensuring performance that meets the demands of modern applications. Our flexible approach lets you mix and match models to suit different use cases, striking the right balance between accuracy and cost. And with open models that enable vendor flexibility and rapid adaptation, you gain resilience to stay ahead of the curve in an ever-changing AI landscape. Streamlining AI Model Deployment with Enterprise-Grade Confidence Customer Challenge Integrating advanced AI models into enterprise workflows has historically been slow and complex. Specialized teams must manage intricate software dependencies, configure infrastructure, and handle ongoing maintenance—all while navigating the risks of deploying unsupported models in mission-critical environments. This complexity slows innovation, drains engineering resources, and increases risk. The Solution: Simplified, Secure Model Deployment with NVIDIA NIM This collaboration simplifies and de-risks AI deployment. The llama-nemotron-embed-1b-v2 model is available as an NVIDIA NIM microservice for secure, reliable deployment across multiple Azure compute platforms. Prebuilt NIM containers for a broad spectrum of AI models and can be deployed with a single command for easy integration into enterprise-grade AI applications using built-in REST APIs of SQL Server 2025 and just a few lines of code, regardless where you run SQL Server workloads and NVIDIA NIM, on premises or in the cloud. NIM containers package the latest AI models together with the best inference technology from NVIDIA and the community and all dependencies into a ready-to-run container, abstracting away the complexity of environment setup so customers can spin up AI services quickly. Furthermore, NVIDIA NIM is enterprise-grade and is continuously managed by NVIDIA with dedicated software branches, rigorous validation processes, and support. As a result, developers can confidently integrate state-of-the-art AI into their data applications. This streamlined approach significantly reduces development overhead and provides the reliability needed for mission-critical enterprise systems. NVIDIA NIM containers are discoverable and deployable via Microsoft Azure AI Foundry’s model catalog. Customer Benefits Rapid deployment with minimal setup means you can start leveraging AI without specialized engineering, and SQL Server 2025 makes it even easier with built-in support for AI workloads and native REST APIs. Enterprise-grade security and monitoring ensure safe, reliable operations, while SQL Server’s integration with Entra ID and advanced compliance features provide added protection. Direct integration into SQL workflows reduces complexity and risk, and with SQL Server’s hybrid flexibility, you can run seamlessly across on-premises and cloud environments—simplifying modernization while maintaining control. Innovating Without Compromise on Security or Flexibility Customer Challenge Organizations in regulated industries often face a tough choice: adopt powerful AI or maintain strict data residency and compliance. Moving sensitive data to external services is often not an option, and many companies run AI inference workloads both in the cloud and on-premises to balance scalability, privacy, regulatory compliance, and low-latency requirements. The Solution: Flexible, Secure Integration—On-Premises and Cloud SQL Server 2025 enables organizations in regulated environments to securely integrate locally hosted AI models, ensuring data residency and compliance while minimizing network overhead. This architecture boosts throughput by keeping sensitive data on-premises and leveraging SQL Server’s native extensibility for direct model invocation. With SQL Server 2025 and Nemotron RAG, deployed as NVIDIA NIM microservices, you get the best of both worlds. This solution can be seamlessly deployed in the cloud with serverless NVIDIA GPUs on Azure Container Apps (ACA) or on-premises with NVIDIA GPUs on Azure Local. Sensitive data never leaves your secure environment, allowing you to harness the full power of Nemotron models while maintaining complete data sovereignty and meeting the strictest compliance mandates. Customer Benefits SQL Server 2025 helps you maintain compliance by supporting data residency and meeting regulatory standard requirements across regions. Sensitive data stays protected on-premises with enterprise-grade security, including consistent access controls, ledger support, and advanced encryption to minimize risk. At the same time, SQL Server’s hybrid flexibility lets you deploy AI workloads wherever they’re needed—on-premises, in the cloud, or across a hybrid environment—while leveraging built-in AI features like vector search and secure integration with locally hosted models for performance and control. Conclusion: Powering the Next Wave of Enterprise AI The collaboration between Microsoft and NVIDIA is more than a technical integration. It’s designed to help enterprises overcome the toughest challenges in AI deployment. By streamlining vector embedding and vector search, delivering enterprise-grade performance, and enabling secure, flexible integration across cloud and on-premises environments, this joint solution empowers organizations to unlock the full value of their data. Whether you’re building conversational AI, automating document analysis, or driving predictive insights, SQL Server 2025 and NVIDIA Nemotron RAG models, deployed as NIM, provide the tools you need to innovate with confidence. The future of enterprise AI is here and it’s flexible, secure, and built for real business impact. Get started today: Learn more about SQL Server 2025 and download it today Learn more about our joint solution from NVIDIA’s Technical Blog GitHub: Microsoft SQL Server 2025 and NVIDIA Nemotron RAGSQL Server 2025 Preview: Now Supporting Ubuntu 24.04 and TLS 1.3
We are excited to introduce two key enhancements in the SQL Server 2025 Release Candidate 0 (RC0) for Linux: Ubuntu 24.04 and the addition of Transport Layer Security (TLS) 1.3 support. These updates enable developers, database administrators, and IT professionals to leverage the latest open-source technologies and security protocols, strengthening their data platforms. Ubuntu 24.04 Support in SQL Server 2025 RC0 SQL Server 2025 Preview now supports Ubuntu 24.04. This enables seamless deployment in Dev/Test environments using the Enterprise Evaluation Edition, which is valid for 180 days. Note: Production workloads on Ubuntu 24.04 are not yet supported; for production, use SQL Server 2022 on Ubuntu 22.04 or RHEL 9. How to Deploy SQL Server 2025 RC0 on Ubuntu 24.04 Getting started is easy! You can follow our Quickstart: Install SQL Server and create a database on Ubuntu to walks through everything—from prepping your system to installing and configuring SQL Server on Ubuntu. In this demo, I'll show you how to deploy SQL Server 2025 RC0 on Ubuntu 24.04 running inside WSL2. I've already set up Ubuntu 24.04 on WSL2 and Docker Desktop to manage containers. With just two commands, I was able to launch SQL Server 2025 RC0 in a container. I then connected to it using SQL Server Management Studio (SSMS), where you can see the version information displayed, confirming a successful deployment. lsb_release -a docker pull mcr.microsoft.com/mssql/server:2025-RC0-ubuntu-24.04 docker run -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=<password>" \ -e "MSSQL_AGENT_ENABLED=true" \ -p 14333:1433 --name sql2025preview --hostname sql2025preview \ -d mcr.microsoft.com/mssql/server:2025-RC0-ubuntu-24.04 Here is the snippet of SQL Server ERRORLOG This shows the initial startup messages and confirms the SQL Server version running inside the container. TLS 1.3 Support in SQL Server 2025 RC0 Starting in SQL Server 2025 Preview (RC0), TLS 1.3 is enabled by default. To enable and validate TLS 1.3 for your SQL Server instance, follow the Learn guide: Encrypt Connections to SQL Server on Linux - SQL Server | Microsoft Learn Conclusion The addition of Ubuntu 24.04 and TLS 1.3 support in SQL Server 2025 Preview marks a significant step forward in providing modern, secure, and flexible data platform options. We encourage you to try out these new capabilities and share your feedback as we continue to improve SQL Server for the Linux ecosystem. We recommend you use any of the following options that suits you the best. 1) Send us an email with your feedback to sqlpreviewpackage@microsoft.com. 2) Another option would be to submit your comments directly on Azure Ideas (Use the SQL Server on Linux Group on the left side of the page) 3) Alternatively, you can open issues related to the preview packages Issues · microsoft/mssql-docker (github.com) on GitHub. We hope you give SQL Server 2025 preview on Ubuntu 24.04 a try and let us know what you think!Enabling Azure Key Vault for SQL Server on Linux
Enhancing Security with EKM using Azure Key Vault in SQL Server on Linux: We’re excited to announce that Extensible Key Management (EKM) using Azure Key Vault in SQL Server on Linux is now generally available from SQL Server 2022 CU12 onwards, which allows you to manage encryption keys outside of SQL Server using Azure Key Vaults. In this blog post, we’ll explore how to leverage Azure Key Vault as an EKM provider for SQL Server on Linux. Azure Key Vault: The Bridge to Enhanced Security is a cloud-based service that securely stores keys, secrets, and certificates. By integrating Azure Key Vault with SQL Server, you can benefit from its scalability, high performance, and high availability. Refer Set up Transparent Data Encryption (TDE) Extensible Key Management with Azure Key Vault - SQL Server | Microsoft Learn for more details. Setting Up EKM with Azure Key Vault Here’s a streamlined version of the setup process for EKM with Azure Key Vault on SQL Server for Linux: Initialize a Microsoft Entra service principal. Establish an Azure Key Vault. Set up SQL Server for EKM and register the SQL Server Connector. Finalize SQL Server configuration. The full guide for setting up AKV with SQL Server on Linux is available here Set up Transparent Data Encryption (TDE) Extensible Key Management with Azure Key Vault - SQL Server | Microsoft Learn . For SQL on Linux, omit steps 3 and 4 and proceed directly to step 5. I’ve included screenshots below for your quick reference that covers the SQL Server configuration to use AKV. Run the below commands to enable EKM in SQL Server and register the SQL Server Connector as EKM provider. Please note: SQL Server requires manual rotation of the TDE certificate or asymmetric key, as it doesn’t rotate them automatically. Regular key rotation is essential for maintaining security and effective key management. Conclusion Using Azure Key Vault for EKM with SQL Server on Linux boosts security, streamlines key management, and supports compliance. With data protection being paramount, Azure Key Vault’s integration offers a robust solution. Stay tuned for more insights on SQL Server on Linux! :old_key:️:locked: Official Documentation: Extensible Key Management using Azure Key Vault - SQL Server Setup Steps for Extensible Key Management Using the Azure Key Vault Azure Key Vault Integration for SQL Server on Azure VMsEntra Authentication in Arc enabled SQL Server 2025 - Windows
This blog will discuss the newly added, “Primary managed identity” in Arc enabled SQL Server 2025 by Microsoft Entra, which enables credential free authentication for both inbound & outbound communications. The Primary Managed Identity pertains to the identity of the Arc machine, which is registered by the Arc machine agent with Microsoft Entra. SQL Server can utilize this identity to authenticate with other Azure services. Associate a “Primary managed identity” to the SQL Server: Arc enabled windows machine, have a managed identity created for them. SQL Server 2025 can now use that identity to establish a trust relationship with Microsoft Entra. You can attach this identity to SQL Server by opting for it from the Azure portal. To activate the primary managed identity from Azure, as a pre-requisite, you need the latest Azure extension for SQL Server release. Note: We keep improving the Azure portal user experience and you might see slight differences depending on when you are reading this blog post. A primary managed identity is necessary for both outbound and inbound communication. Alternatively, you can just Arc enable the host machine and use the manual set up for the managed identity feature. This eliminates the need for the Azure extension for SQL Server, which you must uninstall. With this approach you will not be able to use the Azure portal for Microsoft Entra features. Outbound Communication: You can now use this Primary managed identity to connect the SQL Server 2025 to Azure resources like Azure Storage and Azure Key vault. Follow this to set up the backup to an Azure storage URL, and EKM with Azure key vault. Inbound Communication: You can also use the primary managed identity to create Entra based users and logins to connect to SQL Server 2025. For this you will need to grant these graph API permissions. User.Read.All, GroupMember.Read.All, and Application.Read.All Read more here for the details and limitations on this managed identity setup. For Arc-enabled SQL Server 2025, we recommend using managed identity as it is more secure than the credential-based setup from SQL Server 2022. Although you can still register your SQL Server 2025 with Microsoft Entra for inbound communication only, the Azure portal for SQL 2025 will no longer support the App-registration method. Next steps: To proceed, please obtain your SQL Server 2025 from here to explore all the SQL Server 2025 features available in the public preview version. If you are using an antivirus software, please refer to these instructions.SQL Server 2025 - AI ready enterprise database from ground to cloud
The new version of SQL Server is designed to be an AI-ready enterprise database platform, integrating seamlessly from ground to cloud to Fabric. In this blog, we will explore the key features and enhancements that make SQL Server 2025 a game-changer for developers, database administrators, and organizations. The new capabilities build upon more than three decades of SQL Server innovation in performance, availability, reliability, and security, adding a host of new features that empower developers, protect data, and enable seamless analytics through the Microsoft Fabric integration. AI integration SQL Server 2025 offers features to support enterprise applications. This version integrates AI with customer data using AI capabilities within the SQL engine, ensuring that AI models remain isolated securely. The built-in vector data type allows hybrid AI vector searches, combining vectors with SQL data for efficient and accurate data retrieval. This integration facilitates AI application development and retrieval-augmented generation (RAG) patterns, and AI Agents using the familiar T-SQL syntax. The new vector data type stores vector embeddings alongside relational data, enabling semantically related searches within SQL Server. New vector functions perform operations on vectors in binary format, enabling applications to store and manipulate vectors directly within the SQL database engine. SQL Server 2025 includes T-SQL functions that provide the necessary tools for working with embeddings, without requiring detailed knowledge of their usage. Vectors enable AI models to identify similar data using the K-Nearest Neighbors (KNN) algorithm, with metrics like dot product or cosine similarity. To enhance scalability, SQL Server 2025 incorporates Approximate Vector Index and Vector Search, leveraging Approximate Nearest Neighbors (ANN) for faster, resource-efficient, and accurate results. SQL Server 2025 introduces advanced AI model management capabilities designed to enhance the efficiency and security of interacting with Azure OpenAI and other AI models. SQL Server 2025 provides options for deploying AI models either on-premises or in the cloud, with compatibility for Azure OpenAI, OpenAI endpoints, and Ollama. With all these capabilities, SQL Server 2025's hybrid search represents a paradigm shift in how organizations access and utilize data. Through a blend of keyword and vector searches, businesses can unlock deeper insights, improve customer satisfaction, and harness the full potential of their data assets. Our customer, Kramer & Crew GmbH & Co, who participated in our Early Adoption Program (EAP) aka private preview shared us below. "Joining the EAP was a great opportunity to explore the new AI, security, performance, Fabric, and Azure Arc features! With the new semantic search and RAG capabilities in SQL Server 2025, we can empower existing GenAI solutions with data embeddings to create next-generation, more intelligent AI applications. By connecting systems (e.g., ITSM, CRM, ERP, and others), we deliver a seamless, natural conversational experience across enterprise environments." Markus Angenendt, Data Platform Infrastructure Lead, Kramer & Crew GmbH & Co. KG Developer productivity SQL Server 2025 introduces several exciting developer features designed to enhance developer productivity. New GitHub Copilot: GitHub Copilot transforms coding with AI-driven suggestions, streamlining workflows and enhancing efficiency. Its agent mode proposes edits, tests, and validates changes, enabling developers to focus on complex tasks. SQL Server Management Studio (SSMS) 21: Releasing SQL Server Management Studio (SSMS) 21, for general availability (GA). SSMS 21 includes support for SQL Server 2025. The Copilot in SSMS – now available in preview. New Python Driver: The Python driver for SQL Server and Azure SQL offers efficient, asynchronous connectivity across platforms like Windows, Linux, and macOS. It's designed to simplify development and enhance performance for data-driven applications. Standard Developer Edition: SQL Server 2025 Standard Developer Edition is a free edition licensed for development and test purposes. The intent is to enable all features of SQL Server Standard Edition to facilitate the development and testing of new applications that use the Standard Edition in production. This edition complements the existing Enterprise Developer Edition. JSON data type and aggregates: SQL Server 2025 includes a native JSON data type, allowing for more efficient storage and manipulation of JSON data up to 2GB storage per JSON document. This type supports various JSON aggregate functions to facilitate the aggregation of JSON data. Queries over JSON documents can be optimized by creating a JSON index and using JSON functions and methods to modify and search data natively. Regular expressions (RegEx): SQL Server 2025 introduces support for Regular Expressions (RegEx), providing powerful tools for developers to efficiently query and manipulate text data, better matching pattern than “LIKE” operator. External REST endpoint invocation: The sp_invoke_external_rest_endpoint stored procedure allows for the native invocation of any REST endpoints directly from within T-SQL, enabling seamless integration with external web services. Change event streaming (CES): Enables real-time data integration by streaming data changes directly from SQL Server to Azure Event Hubs with Kafka compatibility, facilitating near real-time analytics and event-driven architecture based on Transaction log. Consider using Change Event Streaming for CDC as it eliminates the need for I/O operations, offering a more efficient and streamlined solution for developers. New T-SQL functions: Several new T-SQL functions introduced to simplify complex queries and increase workload performance. For example, the PRODUCT() aggregate function calculates the product of a set of values. New Chinese collations: Support for GB18030-2022 collation standard. Overall, these developer-centric enhancements in SQL Server 2025 streamline the process of building modern, AI powered and data-rich applications. They reduce the need for custom code and encourage a more declarative, in-database approach to data processing, which can lead to simpler architecture and better performance. “The introduction of the new PRODUCT() aggregate function in SQL Server 2025 has streamlined this process, reducing code complexity while improving computational efficiency by over 30%. This enhancement accelerates key economic calculations, including the computation of the U.S. Gross Domestic Product (GDP), and also strengthens organizations’ ability to deliver timely, accurate data to policymakers and to the public." -- David Rozenshtein and Sandip Mehta, IT Modernization Architects, Omnicom Consulting Group” Secure by default SQL Server 2025 delivers a range of advanced security features designed to enhance data protection, authentication, and encryption. Here are the key security enhancements. Stop using client secrets and passwords: SQL Server 2025 supports managed identity authentication enabled by Azure Arc. This feature allows secure authentication for outbound connections to Azure resources and inbound connections for external users. For example, backup to Azure Blob Storage can now use SQL Server managed identity for authentication. Stronger encryption: To protect the key material of a symmetric key SQL Server stores the key material in encrypted form. Historically, this encryption utilized PKCS#1 v1.5 padding mode; Optimized starting with SQL Server 2025, the encryption uses Optimal Asymmetric Encryption Padding (OAEP) for encryption by certificate or asymmetric key. Stronger password encryption: To store a SQL user password we use an iterated hash algorithm, RFC2898, also known as a password-based key derivation function (PBKDF). This algorithm uses SHA-512 hash but hashes the password multiple times (100,000 iterations), significantly slowing down brute-force attacks. This change enhances password protection in response to evolving security threats and helps customers comply with NIST SP 800-63b guidelines. Strict connection encryption: The implementation of Extended TDS 8.0 support and TLS 1.3 for stringent encryption protocols enhances the security of internal component communications within SQL Server 2025. Optimized security cache: When security cache entries are invalidated, only those entries belonging to the impacted login are affected. This minimizes the impact on non-cache permissions validation for unaffected login users. In summary, SQL Server 2025 continues the product’s legacy of top-notch security by incorporating modern identity and encryption practices. By embracing Azure AD, managed identities, and stronger cryptography by default, it helps organizations avoid vulnerabilities and meet compliance requirements more easily, protecting data both at rest and in motion. Mission critical database engine SQL Server 2025 introduces significant performance and reliability enhancements designed to optimize workload efficiency and reduce troubleshooting efforts. Utilize insights gained from prior executions of expressions within queries enhance the performance of future executions. Optional parameter plan optimization helps SQL Server choose the optimal execution plan based on runtime parameter values, reducing performance issues caused by parameter sniffing. Optimized locking improves concurrency by avoiding blocking and lock escalation and reduces lock memory usage. Enhancements in batch mode processing and columnstore indexes further improve SQL Server as a mission-critical database for analytical workloads. Query Store for readable secondaries allows you to monitor and adjust the performance of read-only workloads executing against secondary replicas. In SQL Server 2025 this is enabled by default. Persisted temporary statistics for readable secondaries are now saved to the primary replica, ensuring permanence and avoiding recreation after restarts, which could degrade performance. A new query hint blocks future execution of problematic queries, such as nonessential queries affecting application performance. Optimized Halloween protection reduces tempdb space consumption and improves performance of data modification queries. Tempdb space resource governance improves reliability by restricting workloads from consuming excessive tempdb space. Accelerated database recovery in tempdb provides instantaneous transaction rollback and aggressive log truncation for transactions in tempdb. Fast failover for persistent health issues: The Windows Failover Cluster (WSFC) can be configured to failover the availability group resource promptly upon detection of a persistent health issue for example long I/O . Enhancements have been made to the undo-of-redo process during disaster recovery failover to asynchronous replicas, improving synchronization performance. Internal synchronization mechanisms have been improved to reduce network saturation when the global primary and forwarder replicas are in asynchronous commit mode. Improved health check time-out diagnostics. Configure a distributed availability group between two contained availability groups. The new backup compression algorithm, ZSTD, provides significant enhancements in compression efficiency while utilizing fewer resources. You can now offload FULL, DIFFERENTIAL, and T-LOG backups to a secondary replica in an Always On Availability Group, freeing your primary replica to handle production workloads. Fabric integration and Analytics Database mirroring to Fabric can continuously replicate data from a database in a SQL Server 2025 instance, on-premises or in virtual machines. A mirrored database item is a read-only, continuously replicated copy of your SQL Server database data in OneLake. SQL Server now natively supports querying CSV, Parquet, and Delta files using OPENROWSET, CREATE EXTERNAL TABLE, or CREATE EXTERNAL TABLE commands, without needing PolyBase Query Service. SQL Server on Linux tmfs filesystem is supported for tempdb in SQL Server 2025 on Linux. This enhancement can improve performance for tempdb-heavy workloads by utilizing memory (RAM) instead of disk-based filesystems. Custom password policy enforces a custom password policy for SQL authentication logins in SQL Server on Linux. PolyBase in SQL Server for Linux can now connect to ODBC data sources. Discontinued services Data Quality Services (DQS) is discontinued in this version of SQL Server. We continue to support DQS in SQL Server 2022 (16.x) and earlier versions. Master Data Services (MDS) is discontinued in this version of SQL Server. We continue to support MDS in SQL Server 2022 (16.x) and earlier versions. Get started SQL Server 2025 is not just an iterative update; it’s a substantial upgrade that bridges the worlds of databases and AI, on-premises and cloud. It retains full support for existing applications and T-SQL code, so upgrades can be done with minimal changes. By adopting SQL Server 2025, organizations can answer new questions with their data, serve applications at a greater scale, and integrate more closely with modern data platforms – all while relying on the familiar, reliable foundation that SQL Server has provided for years. Ready to try it out? Get started today: aka.ms/getsqlserver2025. Learn more Microsoft Build 2025: SQL Server 2025: The Database Developer Reimagined Docs: aka.ms/Build/sql2025docs Announcement blog: aka.ms/sqlserver2025 SQL Server homepage: https://www.microsoft.com/en-us/sql-server MSSQL Extension for Visual Studio Code with GitHub Copilot: https://aka.ms/vscode-mssql-copilot
How to create Linked server from SQL Server to Azure SQL Database
Here in this blog, we are going to demo how to configure Linked server from on-prem SQL Server instance to Azure SQL database. I will have a reference to this blog in my subsequent blogs that will soon be available for Dynamic Data Masking & Cross database/server queries. We have taken example of two databases, Database1 (Copy of AdventureWorks 2019) as Azure SQL database & Database2 hosted in On-prem SQL Server Instance. In this demo we will be querying Database1 tables in the context of Database2. Database1: Azure SQL database Database2: SQL Server Instance on-prem Here is the sequence of steps that you need to follow to configure Linked Server using SSMS tool. Step-1: Connect to SQL Server Instance in SSMS tool and go to Object Explorer. Expand the Server Objects, right click on Linked Server and create a New Linked Server. Step-2: Go to General tab in the new Linked Server window. Under the Server type section, choose the Other data source option. Give a suitable name to the Linked Server as per your choice in the Linked Server section. Choose the “Microsoft OLE DB Provider SQL Server” in the Provider dropdown. In the Data source section, specify the Azure database logical server name for e.g., logicalservername.database.windows.net. You just need to change the logicalservername to the actual Azure server name which you can get from the Azure portal. Enter the Azure database name that you want to create the linked server to in the catalog field. Step-3: Now go to Security tab and choose the option “Be made using this security context”. Enter the SQL login credentials which is already present on the Azure DB server and has access to Database1. Please note this option is the least secure way to address the security configuration of Linked Server as any user who uses the Linked Server will be authenticated on the remote server using credentials provided here. Use of this option should be limited to testing environment. Step-4: Once the Linked Server is successfully created you can see it when you expand Linked Server section in Object Explorer and expand it further to view the list of tables. Step-5: Open a new query window in SSMS and switch to Database2 context on the on-prem SQL Server. Run the SELECT query to fetch data using the Linked Server that you just created in the previous steps. select * from [AZURE DATABASE DDMTEST].[Database1].[Person].[PersonPhone] Hope you find the blog helpful. Please share your questions or feedback.
