ABORT_QUERY_EXECUTION query hint - public preview
We are pleased to announce the public preview of a new query hint, ABORT_QUERY_EXECUTION. The hint is intended to be used as a Query Store hint to let administrators block future execution of known problematic queries, for example non-essential queries causing high resource consumption and affecting application workloads. The hint is now available in Azure SQL Database for all databases without restrictions. The hint will later be available in Azure SQL Managed Instance with the always-up-to-date update policy, as well as in a future version of SQL Server. For more information, see Block future execution of problematic queries in documentation. Update 2025-10-06: The ABORT_QUERY_EXECUTION hint is now generally available. Frequently Asked Questions Is this supported by Microsoft Support during public preview? Yes, just like other query hints. How do I use this? Use Query Store catalog views or the Query Store UI in SSMS to find the query ID of the query you want to block and execute sys.sp_query_store_set_hints specifying that query ID as a parameter. For example: EXEC sys.sp_query_store_set_hints @query_id = 17, @query_hints = N'OPTION (USE HINT (''ABORT_QUERY_EXECUTION''))'; What happens when a query with this hint is executed? This hint is intended to be used as a Query Store hint but can be specified directly as well. In either case, the query fails immediately with error 8778, severity 16: Query execution has been aborted because the ABORT_QUERY_EXECUTION hint was specified. How do I unblock a query? Remove the hint by executing sys.sp_query_store_clear_hints with the query ID value of the query you want to unblock passed via the @query_id parameter. Can I block a query that is not in Query Store? No. At least one execution of the query must be recorded in Query Store. That query execution does not have to be successful. This means that a query that started executing but was canceled or timed out can be blocked too. When I add the hint, does it abort any currently executing queries? No. The hint only aborts future query executions. You can use KILL to abort currently executing queries. What permissions are required to use this? As with all other Query Store hints, the ALTER permission on the database is required to set and clear the hint. Can I block all queries matching a query hash? Not directly. As with all other Query Store hints, you must use a query ID to set and clear a hint. However, you can create automation that will periodically find all new query IDs matching a given query hash and block them. Can I find all blocked queries in Query Store? Yes, by executing the following query: SELECT qsh.query_id, q.query_hash, qt.query_sql_text FROM sys.query_store_query_hints AS qsh INNER JOIN sys.query_store_query AS q ON qsh.query_id = q.query_id INNER JOIN sys.query_store_query_text AS qt ON q.query_text_id = qt.query_text_id WHERE UPPER(qsh.query_hint_text) LIKE '%ABORT[_]QUERY[_]EXECUTION%' Where do I send feedback about this hint? The preferred feedback channel is via https://aka.ms/sqlfeedback. Feedback sent that way is public and can be voted and commented on by other SQL community members. You can also leave comments on this blog post or email us at intelligentqp@microsoft.com.
Stream data in near real time from SQL to Azure Event Hubs - Public preview
If near-real time integration is something you are looking to implement and you were looking for a simpler way to get the data out of SQL, keep reading. SQL is making it easier to integrate and Change Event Streaming is a feature continuing this trend. Modern applications and analytics platforms increasingly rely on event-driven architectures and real-time data pipelines. As the businesses speed up, real time decisioning is becoming especially important. Traditionally, capturing changes from a relational database requires complex ETL jobs, periodic polling, or third-party tools. These approaches often consume significant cycles of the data source, introduce operational overhead, and pose challenges with scalability, especially if you need one data source to feed into multiple destinations. In this context, we are happy to release Change Event Streaming ("CES") feature into Public Preview for Azure SQL Database. This feature enables you to stream row-level changes - inserts, updates, and deletes - from your database directly to Azure Event Hubs in near real time. Change Event Streaming addresses the above challenges by: Reducing latency: Changes are streamed (pushed by SQL) as they happen. This is in contrast with traditional CDC (change data capture) or CT (change tracking) based approaches, where an external component needs to poll SQL at regular intervals. Traditional approaches allow you to increase polling frequency, but it gets difficult to find a sweet spot between minimal latency and minimal overhead due to too frequent polls. Simplifying architecture: No need for Change Data Capture (CDC), Change Tracking, custom polling or external connectors - SQL streams directly to configured destination. This means simpler security profile (fewer authentication points), fewer failure points, easier monitoring, lower skill bar to deploy and run the service. No need to worry about cleanup jobs, etc. SQL keeps track of which changes are successfully received by the destination, handles the retry logic and releases log truncation point. Finally, with CES you have fewer components to procure and get approved for production use. Decoupling: The integration is done on the database level. This eliminates the problem of dual writes - the changes are streamed at transaction boundaries, once your source of truth (the database) has saved the changes. You do not need to modify your app workloads to get the data streamed - you tap right onto the data layer - this is useful if your apps are dated and do not possess real-time integration capabilities. In case of some 3rd party apps, you may not even have an option to do anything other than database level integration, and CES makes it simpler. Also, the publishing database does not concern itself with the final destination for the data - Stream the data once to the common message bus, and you can consume it by multiple downstream systems, irrespective of their number or capacity - the (number of) consumers does not affect publishing load on the SQL side. Serving consumers is handled by the message bus, Azure Event Hubs, which is purpose built for high throughput data transfers. onceptually visualizing data flow from SQL Server, with an arrow towards Azure Event Hubs, from where a number of arrows point to different final destinations. Key Scenarios for CES Event-driven microservices: They need to exchange data, typically thru a common message bus. With CES, you can have automated data publishing from each of the microservices. This allows you to trigger business processes immediately when data changes. Real-time analytics: Stream operational data into platforms like Fabric Real Time Intelligence or Azure Stream Analytics for quick insights. Breaking down the monoliths: Typical monolithic systems with complex schemas, sitting on top of a single database can be broken down one piece at a time: create a new component (typically a microservice), set up the streaming from the relevant tables on the monolith database and tap into the stream by the new components. You can then test run the components, validate the results against the original monolith, and cutover when you build the confidence that the new component is stable. Cache and search index updates: Keep distributed caches and search indexes in sync without custom triggers. Data lake ingestion: Capture changes continuously into storage for incremental processing. Data availability: This is not a scenario per se, but the amount of data you can tap into for business process mining or intelligence in general goes up whenever you plug another database into the message bus. E.g. You plug in your eCommerce system to the message bus to integrate with Shipping providers, and consequently, the same data stream is immediately available for any other systems to tap into. How It Works CES uses transaction log-based capture to stream changes with minimal impact on your workload. Events are published in a structured JSON format following the CloudEvents standard, including operation type, primary key, and before/after values. You can configure CES to target Azure Event Hubs via AMQP or Kafka protocols. For details on configuration, message format, and FAQs, see the official documentation: Feature Overview CES: Frequently Asked Questions Get Started Public preview CES is available today in public preview for Azure SQL Database and as a preview feature in SQL Server 2025. Private preview CES is also available as a private preview for Azure SQL Managed Instance and Fabric SQL database: you can request to join the private preview by signing up here: https://aka.ms/sql-ces-signup We encourage you to try the feature out and start building real-time integrations on top of your existing data. We welcome your feedback—please share your experience through Azure Feedback portal or support channels. The comments below on this blog post will also be monitored, if you want to engage with us. Finally, CES team can be reached via email: sqlcesfeedback [at] microsoft [dot] com. Useful resources Free Azure SQL Database. Free Azure SQL Managed Instance.General Availability announcement of Backup/Restore capabilities in SQL Server 2025
Over the past several months, we’ve heard from countless customers who are eager for more robust options to protect, compress, and safeguard their SQL Server data. Since introducing these features in public preview, organizations of all sizes have validated their value in real-world workloads and provided invaluable feedback. Today, we are thrilled to announce the General Availability (GA) of three powerful SQL Server 2025 features: Backups on Secondary for Always On Availability Groups, ZSTD Compression, and Immutable Backups for Ransomware Protection. These advancements are now ready for production use, built around the needs and requests of the SQL Server customers. Backups on Secondary for SQL Server Always On Availability Groups Previously in preview, this feature now reaches GA, allowing you to offload backup operations to secondary replicas in Always On Availability Groups. This enhancement optimizes resource utilization and minimizes overhead on primary replicas, ensuring better performance for mission-critical workloads. What’s New in GA: Improved reliability and support for production environments. Key Benefits: Comprehensive support: Full, differential, and transaction log backups are now fully supported on secondary replicas—not just COPY_ONLY and transaction logs. Reduced impact on primary replica performance. Simplified high-availability strategies. Learn more from the original announcement: Introducing Backups on Secondary for SQL Server Always On Availability Groups. ZSTD Compression in SQL Server 2025 The GA of ZSTD compression brings modern, efficient data compression to SQL Server. ZSTD compression introduces industry-leading performance and efficiency, letting you save storage and speed up workloads. What’s New in GA: Full production support for ZSTD across key workloads. Key Benefits: Faster compression and decompression compared to legacy algorithms. Lower storage footprint without sacrificing performance. Choose your algorithm: ZSTD is now a standard option right alongside MS_XPRESS for row, page, and backup compression. Tunable compression levels: Administrators can select from LOW, MEDIUM, or HIGH to balance resource use and savings. Ideal for large-scale data environments. Explore the preview details: ZSTD Compression in SQL Server 2025. Backups to immutable storage: A Powerful Shield Against Ransomware Your backups are now safer than ever. Thanks to native support for immutability with Azure Blob Storage, backup files can be rendered tamper-proof—protecting them from ransomware or even accidental deletion. Key Benefits: Strong defense against ransomware and malicious tampering. Compliance with regulatory requirements for data integrity. Peace of mind for critical backup strategies. Read the detailed use-case and how-to: Immutability: A Powerful Shield Against Ransomware in SQL Environments. These features collectively empower organizations to: Optimize performance and resource utilization. Reduce operational costs through efficient compression. Strengthen security posture against evolving threats. Get Started Today These features are available to all SQL Server 2025 customers. Ready to elevate your data protection, efficiency, and compliance posture? Access the official SQL Server 2025 documentation for step-by-step guides via visit Microsoft Learn. Review upgrade guidance and best practices. Explore real-world configurations and FAQs. Upgrade now and unlock the next level of resilience, efficiency, and security for your SQL Server workloads!General Availability Announcement: Regex Support in SQL Server 2025 & Azure SQL
We’re excited to announce the General Availability (GA) of native Regex support in SQL Server 2025 and Azure SQL — a long-awaited capability that brings powerful pattern matching directly into T-SQL. This release marks a significant milestone in modernizing string operations and enabling advanced text processing scenarios natively within the database engine. What is Regex? The other day, while building LEGO with my 3-year-old — an activity that’s equal parts joy and chaos — I spent minutes digging for one tiny piece and thought, “If only Regex worked on LEGO.” That moment of playful frustration turned into a perfect metaphor. Think of your LEGO box as a pile of data — a colorful jumble of tiny pieces. Now imagine trying to find every little brick from a specific LEGO set your kid mixed into the pile. That’s tricky — you’d have to sift through each piece one by one. But what if you had a smart filter that instantly found exactly those pieces? That’s what Regex (short for Regular Expressions) does for your data. It’s a powerful pattern-matching tool that helps you search, extract, and transform text with precision. With Regex now natively supported in SQL Server 2025 and Azure SQL, this capability is built directly into T-SQL — no external languages or workarounds required. What can Regex help you do? Regex can help you tackle a wide range of data challenges, including: Enhancing data quality and accuracy by validating and correcting formats like phone numbers, email addresses, zip codes, and more. Extracting valuable insights by identifying and grouping specific text patterns such as keywords, hashtags, or mentions. Transforming and standardizing data by replacing, splitting, or joining text patterns — useful for handling abbreviations, acronyms, or synonyms. Cleaning and optimizing data by removing unwanted patterns like extra whitespace, punctuation, or duplicates. Meet the new Regex functions in T-SQL SQL Server 2025 introduces seven new T-SQL Regex functions, grouped into two categories: scalar functions (return a value per row) and table-valued functions (TVFs) (return a set of rows). Here’s a quick overview: Function Type Description REGEXP_LIKE Scalar Returns TRUE if the input string matches the Regex pattern REGEXP_COUNT Scalar Counts the number of times a pattern occurs in a string REGEXP_INSTR Scalar Returns the position of a pattern match within a string REGEXP_REPLACE Scalar Replaces substrings that match a pattern with a replacement string REGEXP_SUBSTR Scalar Extracts a substring that matches a pattern REGEXP_MATCHES TVF Returns a table of all matches including substrings and their positions REGEXP_SPLIT_TO_TABLE TVF Splits a string into rows using a Regex delimiter These functions follow the POSIX standard and support most of the PCRE/PCRE2 flavor of regular expression syntax, making them compatible with most modern Regex engines and tools. They support common features like: Character classes (\d, \w, etc.) Quantifiers (+, *, {n}) Alternation (|) Capture groups ((...)) You can also use Regex flags to modify behavior: 'i' – Case-insensitive matching 'm' – Multi-line mode (^ and $ match line boundaries) 's' – Dot matches newline 'c' – Case-sensitive matching (default) Examples: Regex in Action Let’s explore how these functions solve tricky real-world data tasks that were hard to do in earlier SQL versions. REGEXP_LIKE: Data Validation — Keeping data in shape Validating formats like email addresses or phone numbers used to require multiple functions or external tools. With REGEXP_LIKE, it’s now a concise query. For example, you can check whether an email contains valid characters before and after the @, followed by a domain with at least two letters like .com, .org, or .co.in. SELECT [Name], Email, CASE WHEN REGEXP_LIKE (Email, '^[A-Za-z0-9._+]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$') THEN 'Valid Email' ELSE 'Invalid Email' END AS IsValidEmail FROM (VALUES ('John Doe', 'john@contoso.com'), ('Alice Smith', 'alice@fabrikam.com'), ('Bob Johnson', 'bob@fabrikam.net'), ('Charlie Brown', 'charlie@contoso.co.in'), ('Eve Jones', 'eve@@contoso.com')) AS e(Name, Email); We can further use REGEXP_LIKE in CHECK constraints to enforce these rules at the column level (so no invalid format ever gets into the table). For instance: CREATE TABLE Employees ( ..., Email VARCHAR (320) CHECK (REGEXP_LIKE (Email, '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$')), Phone VARCHAR (20) CHECK (REGEXP_LIKE (Phone, '^(\d{3})-(\d{3})-(\d{4})$')) ); This level of enforcement significantly enhances data integrity by ensuring that only correctly formatted values are accepted into the database. REGEXP_COUNT: Count JSON object keys Count how many top-level keys exist in a JSON string — no JSON parser needed! SELECT JsonData, REGEXP_COUNT(JsonData, '"[^"]+"\s*:', 1, 'i') AS NumKeys FROM (VALUES ('{"name":"Abhiman","role":"PM","location":"Bengaluru"}'), ('{"skills":["SQL","T-SQL","Regex"],"level":"Advanced"}'), ('{"project":{"name":"Regex GA","status":"Live"},"team":["Tejas","UC"]}'), ('{"empty":{}}'), ('{}')) AS t(JsonData); REGEXP_INSTR: Locate patterns in logs Find the position of the first error code (ERR-XXXX) in log messages — even when the pattern appears multiple times or in varying locations. SELECT LogMessage, REGEXP_INSTR(LogMessage, 'ERR-\d{4}', 1, 1, 0, 'i') AS ErrorCodePosition FROM (VALUES ('System initialized. ERR-1001 occurred during startup.'), ('Warning: Disk space low. ERR-2048. Retry failed. ERR-2049.'), ('No errors found.'), ('ERR-0001: Critical failure. ERR-0002: Recovery started.'), ('Startup complete. Monitoring active.')) AS t(LogMessage); REGEXP_REPLACE: Redact sensitive data Mask SSNs and credit card numbers in logs or exports — all with a single, secure query. SELECT sensitive_info, REGEXP_REPLACE(sensitive_info, '(\d{3}-\d{2}-\d{4}|\d{4}-\d{4}-\d{4}-\d{4})', '***-**-****') AS redacted_info FROM (VALUES ('John Doe SSN: 123-45-6789'), ('Credit Card: 9876-5432-1098-7654'), ('SSN: 000-00-0000 and Card: 1111-2222-3333-4444'), ('No sensitive info here'), ('Multiple SSNs: 111-22-3333, 222-33-4444'), ('Card: 1234-5678-9012-3456, SSN: 999-88-7777')) AS t(sensitive_info); REGEXP_SUBSTR: Extract and count email domains Extract domains from email addresses and group users by domain. SELECT REGEXP_SUBSTR(Email, '@(.+)$', 1, 1, 'i', 1) AS Domain, COUNT(*) AS NumUsers FROM (VALUES ('Alice', 'alice@contoso.com'), ('Bob', 'bob@fabrikam.co.in'), ('Charlie', 'charlie@example.com'), ('Diana', 'diana@college.edu'), ('Eve', 'eve@contoso.com'), ('Frank', 'frank@fabrikam.co.in'), ('Grace', 'grace@example.net')) AS e(Name, Email) WHERE REGEXP_LIKE (Email, '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$') GROUP BY REGEXP_SUBSTR(Email, '@(.+)$', 1, 1, 'i', 1); REGEXP_MATCHES: Extract multiple emails from text Extract all email addresses from free-form text like comments or logs — returning each match as a separate row for easy parsing or analysis. SELECT * FROM REGEXP_MATCHES ('Contact us at support@example.com or sales@example.com', '[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}'); This query identifies and returns both email addresses found in the string — no need for loops, manual parsing, or external scripting. REGEXP_SPLIT_TO_TABLE: Break down structured text Split a string into rows using a Regex delimiter — ideal for parsing logs, config entries, or form data. SELECT * FROM REGEXP_SPLIT_TO_TABLE ('Name: John Doe; Email: john.doe@example.com; Phone: 123-456-7890', '; '); This query breaks the input string into rows for each field, making it easier to parse and process the data — especially when dealing with inconsistent or custom delimiters. To explore more examples, syntax options, and usage details, head over to the https://learn.microsoft.com/en-us/sql/t-sql/functions/regular-expressions-functions-transact-sql?view=sql-server-ver17. Conclusion The addition of Regex functionality in SQL Server 2025 and Azure SQL is a major leap forward for developers and DBAs. It eliminates the need for external libraries, CLR integration, or complex workarounds for text processing. With Regex now built into T-SQL, you can: Validate and enforce data formats Sanitize and transform sensitive data Search logs for complex patterns Extract and split structured content And this is just the beginning. Regex opens the door to a whole new level of data quality, text analytics, and developer productivity — all within the database engine. So go ahead and Regex away! Your feedback and partnership continue to drive innovation in Azure SQL and SQL Server — thank you for being part of it.Securing Azure SQL Database with Microsoft Entra Password-less Authentication: Migration Guide
The Secure Future Initiative is Microsoft’s strategic framework for embedding security into every layer of the data platform—from infrastructure to identity. As part of this initiative, Microsoft Entra authentication for Azure SQL Database offers a modern, password less approach to access control that aligns with Zero Trust principles. By leveraging Entra identities, customers benefit from stronger security postures through multifactor authentication, centralized identity governance, and seamless integration with managed identities and service principals. Onboarding Entra authentication enables organizations to reduce reliance on passwords, simplify access management, and improve auditability across hybrid and cloud environments. With broad support across tools and platforms, and growing customer adoption, Entra authentication is a forward-looking investment in secure, scalable data access. Migration Steps Overview Organizations utilizing SQL authentication can strengthen database security by migrating to Entra Id-based authentication. The following steps outline the process. Identify your logins and users – Review the existing SQL databases, along with all related users and logins, to assess what’s needed for migration. Enable Entra auth on Azure SQL logical servers by assigning a Microsoft Entra admin. Identify all permissions associated with the SQL logins & Database users. Recreate SQL logins and users with Microsoft Entra identities. Upgrade application drivers and libraries to min versions & update application connections to SQL Databases to use Entra based managed identities. Update deployments for SQL logical server resources to have Microsoft Entra-only authentication enabled. For all existing Azure SQL Databases, flip to Entra‑only after validation. Enforce Entra-only for all Azure SQL Databases with Azure Policies (deny). Step 1: Identify your logins and users - Use SQL Auditing Consider using SQL Audit to monitor which identities are accessing your databases. Alternatively, you may use other methods or skip this step if you already have full visibility of all your logins. Configure server‑level SQL Auditing. For more information on turning the server level auditing: Configure Auditing for Azure SQL Database series - part1 | Microsoft Community Hub SQL Audit can be enabled on the logical server, which will enable auditing for all existing and new user databases. When you set up auditing, the audit log will be written to your storage account with the SQL Database audit log format. Use sys.fn_get_audit_file_v2 to query the audit logs in SQL. You can join the audit data with sys.server_principals and sys.database_principals to view users and logins connecting to your databases. The following query is an example of how to do this: SELECT (CASE WHEN database_principal_id > 0 THEN dp.type_desc ELSE NULL END) AS db_user_type , (CASE WHEN server_principal_id > 0 THEN sp.type_desc ELSE NULL END) AS srv_login_type , server_principal_name , server_principal_sid , server_principal_id , database_principal_name , database_principal_id , database_name , SUM(CASE WHEN succeeded = 1 THEN 1 ELSE 0 END) AS sucessful_logins , SUM(CASE WHEN succeeded = 0 THEN 1 ELSE 0 END) AS failed_logins FROM sys.fn_get_audit_file_v2( '<Storage_endpoint>/<Container>/<ServerName>', DEFAULT, DEFAULT, '2023-11-17T08:40:40Z', '2023-11-17T09:10:40Z') -- join on database principals (users) metadata LEFT OUTER JOIN sys.database_principals dp ON database_principal_id = dp.principal_id -- join on server principals (logins) metadata LEFT OUTER JOIN sys.server_principals sp ON server_principal_id = sp.principal_id -- filter to actions DBAF (Database Authentication Failed) and DBAS (Database Authentication Succeeded) WHERE (action_id = 'DBAF' OR action_id = 'DBAS') GROUP BY server_principal_name , server_principal_sid , server_principal_id , database_principal_name , database_principal_id , database_name , dp.type_desc , sp.type_desc Step 2: Enable Microsoft Entra authentication (assign admin) Follow this to enable Entra authentication and assign a Microsoft Entra admin at the server. This is mixed mode; existing SQL auth continues to work. WARNING: Do NOT enable Entra‑only (azureADOnlyAuthentications) yet. That comes in Step 7. Entra admin Recommendation: For production environments, it is advisable to utilize an PIM Enabled Entra group as the server administrator for enhanced access control. Step 3: Identity & document existing permissions (SQL Logins & Users) Retrieve a list of all your SQL auth logins. Make sure to run on the master database.: SELECT * FROM sys.sql_logins List all SQL auth users, run the below query on all user Databases. This would list the users per Database. SELECT * FROM sys.database_principals WHERE TYPE = 'S' Note: You may need only the column ‘name’ to identify the users. List permissions per SQL auth user: SELECT database_principals.name , database_principals.principal_id , database_principals.type_desc , database_permissions.permission_name , CASE WHEN class = 0 THEN 'DATABASE' WHEN class = 3 THEN 'SCHEMA: ' + SCHEMA_NAME(major_id) WHEN class = 4 THEN 'Database Principal: ' + USER_NAME(major_id) ELSE OBJECT_SCHEMA_NAME(database_permissions.major_id) + '.' + OBJECT_NAME(database_permissions.major_id) END AS object_name , columns.name AS column_name , database_permissions.state_desc AS permission_type FROM sys.database_principals AS database_principals INNER JOIN sys.database_permissions AS database_permissions ON database_principals.principal_id = database_permissions.grantee_principal_id LEFT JOIN sys.columns AS columns ON database_permissions.major_id = columns.object_id AND database_permissions.minor_id = columns.column_id WHERE type_desc = 'SQL_USER' ORDER BY database_principals.name Step 4: Create SQL users for your Microsoft Entra identities You can create users(preferred) for all Entra identities. Learn more on Create user The "FROM EXTERNAL PROVIDER" clause in TSQL distinguishes Entra users from SQL authentication users. The most straightforward approach to adding Entra users is to use a managed identity for Azure SQL and grant the required three Graph API permissions. These permissions are necessary for Azure SQL to validate Entra users. User.Read.All: Allows access to Microsoft Entra user information. GroupMember.Read.All: Allows access to Microsoft Entra group information. Application.Read.ALL: Allows access to Microsoft Entra service principal (application) information. For creating Entra users with non-unique display names, use Object_Id in the Create User TSQL: -- Retrieve the Object Id from the Entra blade from the Azure portal. CREATE USER [myapp4466e] FROM EXTERNAL PROVIDER WITH OBJECT_ID = 'aaaaaaaa-0000-1111-2222-bbbbbbbbbbbb' For more information on finding the Entra Object ID: Find tenant ID, domain name, user object ID - Partner Center | Microsoft Learn Alternatively, if granting these API permissions to SQL is undesirable, you may add Entra users directly using the T-SQL commands provided below. In these scenarios, Azure SQL will bypass Entra user validation. Create SQL user for managed identity or an application - This T-SQL code snippet establishes a SQL user for an application or managed identity. Please substitute the `MSIname` and `clientId` (note: use the client id, not the object id), variables with the Display Name and Client ID of your managed identity or application. -- Replace the two variables with the managed identity display name and client ID declare @MSIname sysname = '<Managed Identity/App Display Name>' declare @clientId uniqueidentifier = '<Managed Identity/App Client ID>'; -- convert the guid to the right type and create the SQL user declare @castClientId nvarchar(max) = CONVERT(varchar(max), convert (varbinary(16), @clientId), 1); -- Construct command: CREATE USER [@MSIname] WITH SID = @castClientId, TYPE = E; declare nvarchar(max) = N'CREATE USER [' + @MSIname + '] WITH SID = ' + @castClientId + ', TYPE = E;' EXEC (@cmd) For more information on finding the Entra Client ID: Register a client application in Microsoft Entra ID for the Azure Health Data Services | Microsoft Learn Create SQL user for Microsoft Entra user - Use this T-SQL to create a SQL user for a Microsoft Entra account. Enter your username and object Id: -- Replace the two variables with the MS Entra user alias and object ID declare sysname = '<MS Entra user alias>'; -- (e.g., username@contoso.com) declare uniqueidentifier = '<User Object ID>'; -- convert the guid to the right type declare @castObjectId nvarchar(max) = CONVERT(varchar(max), convert (varbinary(16), ), 1); -- Construct command: CREATE USER [@username] WITH SID = @castObjectId, TYPE = E; declare nvarchar(max) = N'CREATE USER [' + + '] WITH SID = ' + @castObjectId + ', TYPE = E;' EXEC (@cmd) Create SQL user for Microsoft Entra group - This T-SQL snippet creates a SQL user for a Microsoft Entra group. Set groupName and object Id to your values. -- Replace the two variables with the MS Entra group display name and object ID declare @groupName sysname = '<MS Entra group display name>'; -- (e.g., ContosoUsersGroup) declare uniqueidentifier = '<Group Object ID>'; -- convert the guid to the right type and create the SQL user declare @castObjectId nvarchar(max) = CONVERT(varchar(max), convert (varbinary(16), ), 1); -- Construct command: CREATE USER [@groupName] WITH SID = @castObjectId, TYPE = X; declare nvarchar(max) = N'CREATE USER [' + @groupName + '] WITH SID = ' + @castObjectId + ', TYPE = X;' EXEC (@cmd) For more information on finding the Entra Object ID: Find tenant ID, domain name, user object ID - Partner Center | Microsoft Learn Validate SQL user creation - When a user is created correctly, the EntraID column in this query shows the user's original MS Entra ID. select CAST(sid as uniqueidentifier) AS EntraID, * from sys.database_principals Assign permissions to Entra based users – After creating Entra users, assign them SQL permissions to read or write by either using GRANT statements or adding them to roles like db_datareader. Refer to your documentation from Step 3, ensuring you include all necessary user permissions for new Entra SQL users and that security policies remain enforced. Step 5: Update Programmatic Connections Change your application connection strings to managed identities for SQL authentication and test each app for Microsoft Entra compatibility. Upgrade your drivers to these versions or newer. JDBC driver version 7.2.0 (Java) ODBC driver version 17.3 (C/C++, COBOL, Perl, PHP, Python) OLE DB driver version 18.3.0 (COM-based applications) Microsoft.Data.SqlClient 5.2.2+ (ADO.NET) Microsoft.EntityFramework.SqlServer 6.5.0 (Entity Framework) System.Data.SqlClient(SDS) doesn't support managed identity; switch to Microsoft.Data.SqlClient(MDS). If you need to port your applications from SDS to MDS the following cheat sheet will be helpful: https://github.com/dotnet/SqlClient/blob/main/porting-cheat-sheet.md. Microsoft.Data.SqlClient also takes a dependency on these packages & most notably the MSAL for .NET (Version 4.56.0+). Here is an example of Azure web application connecting to Azure SQL, using managed identity. Step 6: Validate No Local Auth Traffic Be sure to switch all your connections to managed identity before you redeploy your Azure SQL logical servers with Microsoft Entra-only authentication turned on. Repeat the use of SQL Audit, just as you did in Step 1, but now to confirm that every connection has moved away from SQL authentication. Once your server is up and running with only Entra authentication, any connections still based on SQL authentication will not work, which could disrupt services. Test your systems thoroughly to verify that everything operates correctly. Step 7: Enable Microsoft Entra‑only & disable local auth Once all your connections & applications are built to use managed identity, you can disable the SQL Authentication, by turning the Entra-only authentication via Azure portal, or using the APIs. Step 8: Enforce at scale (Azure Policy) Additionally, after successful migration and validation, it is recommended to deploy the built-in Azure Policy across your subscriptions to ensure that all SQL resources do not use local authentication. During resource creation, Azure SQL instances will be required to have Microsoft Entra-only authentication enabled. This requirement can be enforced through Azure policies. Best Practices for Entra-Enabled Azure SQL Applications Use exponential backoff with decorrelated jitter for retrying transient SQL errors, and set a max retry cap to avoid resource drain. Separate retry logic for connection setup and query execution. Cache and proactively refresh Entra tokens before expiration. Use Microsoft.Data.SqlClient v3.0+ with Azure.Identity for secure token management. Enable connection pooling and use consistent connection strings. Set appropriate timeouts to prevent hanging operations. Handle token/auth failures with targeted remediation, not blanket retries. Apply least-privilege identity principles; avoid global/shared tokens. Monitor retry counts, failures, and token refreshes via telemetry. Maintain auditing for compliance and security. Enforce TLS 1.2+ (Encrypt=True, TrustServerCertificate=False). Prefer pooled over static connections. Log SQL exception codes for precise error handling. Keep libraries and drivers up to date for latest features and resilience. References Use this resource to troubleshoot issues with Entra authentication (previously known as Azure AD Authentication): Troubleshooting problems related to Azure AD authentication with Azure SQL DB and DW | Microsoft Community Hub To add Entra users from an external tenant, invite them as guest users to the Azure SQL Database's Entra administrator tenant. For more information on adding Entra guest users: Quickstart: Add a guest user and send an invitation - Microsoft Entra External ID | Microsoft Learn Conclusion Migrating to Microsoft Entra password-less authentication for Azure SQL Database is a strategic investment in security, compliance, and operational efficiency. By following this guide and adopting best practices, organizations can reduce risk, improve resilience, and future-proof their data platform in alignment with Microsoft’s Secure Future Initiative.The Microsoft.Build.Sql project SDK is now generally available
Your database should be part of a wholistic development process, where iterative development tools are coupled with automation for validation and deployment. As previously announced, the Microsoft.Build.Sql project SDK provides a cross-platform framework for your database-as-code such that the database obejcts are ready to be checked into source control and deployed via pipelines like any other modern application component. Today Microsoft.Build.Sql enters general availability as another step in the evolution of SQL database development. Standardized SQL database as code SQL projects are a .NET-based project type for SQL objects, compiling a folder of SQL scripts into a database artifact (.dacpac) for manual or continuous deployments. As a developer working with SQL projects, you’re creating the T-SQL scripts that define the objects in the database. While the development framework around SQL projects presents a clear build and deploy process for development, there’s no wrong way to incorporate SQL projects into your development cycle. The SQL objects in the project can be manually written or generated via automation, including through the graphical schema compare interfaces or the SqlPackage extract command. Whether you’re developing with SQL Server, Azure SQL, or SQL in Fabric, database development standardizes on a shared project format and the ecosystem of tooling around SQL projects. The same SQL projects tools, like the SqlPackage CLI, can be used to either deploy objects to a database or update those object scripts from a database. Free development tools for SQL projects, like the SQL database projects extension for VS Code and SQL Server Data Tools in Visual Studio, bring the whole development team together. The database model validation of a SQL project build provides early verification of the SQL syntax used in the project, before code is checked in or deployed. Code analysis for antipatterns that impact database design and performance can be enabled as part of the project build and extended. This code analysis capability adds in-depth feedback to your team’s continuous integration or pre-commit checks as part of SQL projects. Objects in a SQL project are database objects you can have confidence in before they’re deployed across your environments. Evolving from original SQL projects SQL projects converted to the Microsoft.Build.Sql SDK benefit from support for .NET 8, enabling cross-platform development and automation environments. While the original SQL project file format explicitly lists each SQL file, SDK-style projects are significantly simplified by including any .sql file in the SQL projects folder structure. Database references enable SQL projects to be constructed for applications where a single project isn’t an effective representation, whether the database includes cross-database references or multiple development cycles contribute to the same database. Incorporate additional objects into a SQL project with database references through project reference, .dacpac artifact reference, and new to Microsoft.Build.Sql, package references. Package references for database objects improve the agility and manageability of the release cycle of your database through improved visibility to versioning and simplified management of the referenced artifacts. Converting existing projects The Microsoft.Build.Sql project SDK is a superset of the functionality of the original SQL projects, enabling you to convert your current projects on a timeline that works best for you. The original SQL projects in SQL Server Data Tools (SSDT) continue to be supported through the Visual Studio lifecycle, providing years of support for your existing original projects. Converting an existing SQL project to a Microsoft.Build.Sql project is currently a manual process to add a single line to the project file and remove several groups of lines. The resulting Microsoft.Build.Sql project file is generally easier to understand and iteratively develop, with significantly fewer merge conflicts than the original SQL projects. A command line tool, DacpacVerify, is now available to validate that your project conversion has completed without degrading the output .dacpac file. By creating a .dacpac before and after you upgrade the project file, you can use DacpacVerify to confirm the database model, database options, pre/post-deployment scripts, and SQLCMD variables match. The road ahead With SQL Server 2025 on the horizon, support for the SQL Server 2025 target platform will be introduced in a future Microsoft.Build.Sql release along with additional improvements to the SDK references. Many Microsoft.Build.Sql releases will coincide with releases to the DacFx .NET library and the SqlPackage CLI with preview releases ahead of general availability releases several times a year. Feature requests and bug reports for the DacFx ecosystem, including Microsoft.Build.Sql, is managed through the GitHub repository. With the v1 GA of Microsoft.Build.Sql, we’re also looking ahead to continued iteration in the development tooling. In Visual Studio, the preview of SDK-style SSDT continues with new features introduced in each Visual Studio release. Plans for Visual Studio include project upgrade assistance in addition to the overall replacement of the existing SQL Server Data Tools. In the SQL projects extension for VS Code, we’re both ensuring SQL projects capabilities from Azure Data Studio are introduced as well as increasing the robustness of the VS Code project build experience. The Microsoft.Build.Sql project SDK empowers database development to integrate with the development cycle, whether you're focused on reporting, web development, AI, or anything else. Use Microsoft.Build.Sql projects to branch, build, commit, and ship your database – get started today from an existing database or with a new project. Get to know SQL projects from the documentation and DevOps samples.Enhanced Server Audit for Azure SQL Database: Greater Performance, Availability and Reliability
We are excited to announce a significant update to the server audit feature for Azure SQL Database. We have re-architected major portions of SQL Auditing resulting in increased availability and reliability of server audits. As an added benefit, we have achieved closer feature alignment with SQL Server and Azure SQL Managed Instance. Database auditing remains unchanged. In the remainder of this blog article, we cover Functional changes Changes Affecting customers Sample queries Call for action Implementation and Notification Time-based Filtering Functional Changes In the current design when server audit is enabled, it triggers a database level audit and executes one audit session for each database. With the new architecture, enabling server audit will create one extended event session at the server level that captures audit events for all databases. This optimizes memory and CPU and is consistent with how auditing works in SQL Server and Azure SQL Managed Instance. Changes Affecting Customers Folder Structure change for storage account Folder structure change for Read-Only replicas Permissions required to view Audit logs One of the primary changes involves the folder structure for audit logs stored in storage account containers. Previously, server audit logs were written to separate folders, one for each database, with the database name serving as the folder name. With the new update, all server audit logs will be consolidated into a single folder which is ‘Master’ folder. This behavior is the same as Azure SQL Managed Instance and SQL Server For Read-Only database replicas, which previously had their logs stored in a read-only folder, those logs will now also be written into the Master folder. You can retrieve these logs by filtering on the new column ‘is_secondary_replica_true’. Please note that the audit logs generated after deployment will adhere to the new folder structure, while the existing audit logs will stay in their current folders until their retention periods expire. Sample Queries To help you adopt these changes in your workflows, here are some sample queries: Current New To Query audit logs for a specific database called "test" SELECT * FROM sys.fn_get_audit_file ('https://testaudit.blob.core.windows.net/sqldbauditlogs/auditpoc/test/ SqlDbAuditing_ServerAudit_NoRetention/2023-01-29/07_06_40_590_0.xel', default, default) SELECT * FROM sys.fn_get_audit_file ('https://testaudit.blob.core.windows.net/sqldbauditlogs/auditpoc/master/SqlDbAuditing_ServerAudit_NoRetention/2023-01-29/07_06_40_590_0.xel', default, default) WHERE database_name = 'test'; To query audit logs for test database from read only replica SELECT * FROM sys.fn_get_audit_file ('https://testaudit.blob.core.windows.net/sqldbauditlogs/auditpoc/test/SqlDbAuditing_ServerAudit_NoRetention/2023-01-29/RO/07_06_40_590_0.xel', default, default) SELECT * FROM sys.fn_get_audit_file ('https://testaudit.blob.core.windows.net/sqldbauditlogs/auditpoc/master/SqlDbAuditing_ServerAudit_NoRetention/2023-01-29/07_06_40_590_0.xel', default, default) WHERE is_secondary_replica_true = 'true'; Permissions Control database on user database VIEW Database SECURITY AUDIT permission in User Databases Implementation and Notifications We are rolling out this change region-wise. Subscription owners will receive notifications with the subject “Update your scripts to point to a new folder for server level audit logs” for each region as the update is implemented. It is important to update any scripts that refer to the folder structure to retrieve audit logs based on the database name for the specific region. Note that this change applies only to server-level auditing; database auditing remains unchanged. Call for Action These actions apply only to customers who are using storage account targets. No action is needed for customers using Log Analytics or Event hubs. Folder references: Change the reference for audit logs from the database name folder to the Master folder and use specific filters to retrieve logs for a required database. Read -Only Database Replicas: Update references for audit logs from the Read-Only replica folder to the Master folder and filter using the new parameter as shown in the examples. Permissions: Ensure you have the necessary control server permissions to review the audit logs for each database using fn_get_audit_file. Manual Queries This update also applies to manual queries where you use fn_get_audit_file to retrieve audit logs from the storage account Time-based filtering To enhance your ability to query audit logs using filters, consider using efficient time-based filtering with the fn_get_audit_file_v2 function. This function allows you to retrieve audit log data with improved filtering capabilities. For more details, refer to the official documentation here.Preview release of SDK-style SQL projects in Visual Studio 2022
SQL Server Data Tools (SSDT) in Visual Studio provides a development environment for SQL Server, Azure SQL, and Synapse Data Warehouse databases based on SQL projects. SQL projects enables database development to directly integrate with DevOps workflows, including functionality for connected databases to be converted into code and for databases to be updated from the SQL projects build artifact (.dacpac). Microsoft.Build.Sql is a project SDK that modernizes the well-established capabilities of SQL Server Data Tools, improving the flexibility and compatibility of SQL projects. In the preview 2 release of Visual Studio 17.12, Microsoft.Build.Sql SDK-style SQL projects begin their preview availability in Visual Studio as the component “SQL Server Data Tools, SDK-style (preview).”Announcing General Availability of UNISTR function and ANSI SQL || Operator in Azure SQL
We’re excited to announce the General Availability (GA) of two long-standing capabilities that address critical needs for SQL developers and enterprise customers in Azure SQL Database and Azure SQL Managed Instance, configured with the Always-up-to-date update policy: UNISTR function for Unicode character representation ANSI SQL string concatenation operator (`||`) for standard-compliant string operations These additions improve ANSI SQL compliance in Azure SQL, streamline migration from other platforms, and boost developer productivity. UNISTR function: Unicode made easy The UNISTR function provides support for Unicode string literals by letting you specify the Unicode encoding value of characters in the string, making it especially useful for working with international or special characters. If you want to include characters like emojis, accented letters, or symbols from non-Latin scripts, UNISTR lets you encode them directly using their Unicode values. Returns Unicode characters based on the input expression, following the Unicode standard. Supports escape sequences: \xxxx for UTF-16 codepoints. \+xxxxxx for full Unicode codepoints. Enables easy integration of complex Unicode characters into strings. You can look up Unicode codepoint values in the Unicode Code Charts. Comparison with NCHAR NCHAR: Converts a single Unicode value to a character. UNISTR: Handles multiple Unicode values and escape sequences, offering greater flexibility for constructing strings with diverse characters. Syntax UNISTR ( 'character_expression' [ , 'unicode_escape_character' ] ) The data type of character_expression could be char, nchar, varchar, or nvarchar. For char and varchar data types, the collation should be a valid UTF-8 collation only. A single character representing a user-defined Unicode escape sequence. If not supplied, the default value is \. Examples Example #1: In this example, Unicode value used with text literal: SELECT UNISTR (N'I \2764 Azure SQL.') AS string_with_unicode Results: I ❤️ Azure SQL. Example #2: In this example, we construct a non-Latin script string using Unicode escape sequence. SELECT UNISTR(N'\3053\3093\306B\3061\306F') AS Japanese_Greeting; Results: こんにちは ANSI SQL || Operator: A cleaner way to concatenate The || operator is now supported for string concatenation, offering a more readable and standard-compliant alternative to existing methods like + and CONCAT(). The || operator concatenates two or more characters or binary strings, columns, or a combination of strings and column names into one expression. The || operator does not honor the SET CONCAT_NULL_YIELDS_NULL option and always behaves as if the ANSI SQL behavior is enabled, yielding NULL if any of the inputs is NULL. This operator will work with character strings or binary data of any supported SQL Server collation. The ||operator supports compound assignment ||= similar to +=. If the operands are of incompatible collation, then an error will be thrown. The collation behavior is identical to the CONCAT function of character string data. Syntax expression || expression The expression is a character or binary expression. Both expressions must be of the same data type, or one expression must be able to be implicitly converted to the data type of the other expression. If one operand is of binary type, then an unsupported operand type error will be thrown. Examples Example #1: For example, the following query concatenates two strings and returns the result: SELECT 'Hello ' || ' World!'; Results: Hello World! Example #2: In this example, multiple character strings are concatenated. If at least one input is a character string, non-character strings will be implicitly converted to character strings. SELECT -- Combine first and last name 'Josè' || ' Doe' AS full_name, -- Construct a detailed order string with ID, timestamp, and unique identifier 'Order-' || CAST(1001 AS VARCHAR) || '~TS~' || current_timestamp || '~' || NEWID() AS order_details, -- Handle NULL safely in concatenation 'Item-' || NULL AS item_desc; Results: full_name order_details item_desc Josè Doe Order-1001~TS~Jul 29 2025 8:33PM~0387F607-1664-4EEB-A4E8-F16B396DDD73 NULL Example #3: In this example, the use of || operator is unsupported when used with only non-character types or when combining binary data with other types. -- Attempting to concatenate two numeric literals: 1 and 2 SELECT 1 || 2; -- Attempting to concatenate integer, string, and binary literals SELECT 1 || 'a' || 0x4e; Above queries will fail with error messages as below – The data types int and int are incompatible in the concat operator. The data types varchar and varbinary are incompatible in the concat operator. Comparison with + and CONCAT() Let’s explore how || differs from + and CONCAT() in Azure SQL using real examples. Sample queries: -- Binary + String SELECT CONCAT(0x01, 'a'); -- Returns: a SELECT 0x01 || 'a'; -- Returns: Error SELECT 0x01 + 'a'; -- Returns: Error -- Binary + Binary SELECT CONCAT(0x01, 0xff); -- Returns: ÿ SELECT 0x01 || 0xff; -- Returns: 0x01FF SELECT 0x01 + 0xff; -- Returns: 0x01FF -- NULL Handling SELECT CONCAT(NULL, 'a'); -- Returns: 'a' SELECT NULL || 'a'; -- Returns: NULL SELECT NULL + 'a'; -- Returns: NULL -- Numeric + String SELECT CONCAT(1, 'a'); -- Returns: '1a' SELECT 1 || 'a'; -- Returns: '1a' SELECT 1 + 'a'; -- Returns: Error -- Date + String SELECT CONCAT(CURRENT_TIMESTAMP, 'a'); -- Returns: Jul 29 2025 9:29PMa SELECT CURRENT_TIMESTAMP || 'a'; -- Returns: Jul 29 2025 9:29PMa SELECT CURRENT_TIMESTAMP + 'a'; -- Returns: Error Key Differences Feature + Operator CONCAT() Function || Operator ANSI SQL Standard No Yes Yes NULL Handling Returns NULL Ignores NULL/ Empty string Returns NULL Type Safety Not type-safe Type-safe Type-safe Readability Moderate Verbose High Portability Low Moderate High Why these matters Easier Migration – Migrating from other databases becomes smoother with support for UNISTR and ||, reducing the need for syntax rewrites. Global Reach – UNISTR simplifies Unicode handling, making it easier to build apps for international markets. Cleaner Code – The || operator improves readability and aligns with modern SQL practices. Learn more UNISTR (Transact-SQL) – SQL Server | Microsoft Learn || (String Concatenation) (Transact-SQL) – SQL Server | Microsoft Learn ||= (Compound assignment) (Transact-SQL) – SQL Server | Microsoft Learn Conclusion In this blog post, we announced the General Availability (GA) of UNISTR function and ANSI SQL string concatenation operator (||) in Azure SQL. The UNISTR function allows you to escape Unicode characters, making it easier to work with international text. ANSI SQL || operator provides a simple and intuitive way to combine characters or binary data. These enhancements reflect our commitment to making Azure SQL the most developer-friendly, standards-compliant cloud-first database platform. Whether you’re modernizing legacy systems, building multilingual apps, or writing cleaner SQL, these features are designed to make your journey smoother. We hope you will explore these enhancements, apply them in your projects, and share your feedback with us to help us continue improving. Thank you!
