Dynamic Data Masking – What it is, What it isn’t, and How to use it effectively
In this post, we’ll explain the core purpose of Dynamic Data Masking (to ease application development), how it works, and its proper use cases – as well as its limitations. If you’re considering using Dynamic Data Masking or reviewing your data security strategy, this information will help you make informed decisions. What Dynamic Data Masking is designed for Dynamic Data Masking Dynamic Data Masking - SQL Server | Microsoft Learn is a database feature that can be used to alter how certain data elements are presented in query results for users who do not have privileged access or required permission. For example, a query on an email column may return a masked value such as jXXX@XXXX.com rather than the full address, depending on user permissions, while the original data remains unchanged in storage. Masking rules are defined within the database schema and are applied to query results for applicable users at runtime. This approach can simplify application developer’s job and reduce the need for application‑level logic that modifies how sensitive values are displayed across different application(s) or reports. DDM can help prevent accidental or casual exposure of sensitive information. How Does DDM differ from other security features? Dynamic Data Masking affects only what users see in query results—it does not protect the underlying data. Unlike encryption Always Encrypted - SQL Server | Microsoft Learn or Row‑Level security Row-Level Security - SQL Server | Microsoft Learn, DDM does not encrypt data, filter rows, or override SQL permissions. Users with elevated privileges (such as UNMASK, db_owner, or sysadmin) always see unmasked data or can modify or remove masking rules. What DDM doesn’t protect against Because Dynamic Data Masking is applied when query results are returned, there are several considerations to be aware of: Inference through queries: In some scenarios, users with database access may be able to make inferences about masked values by applying query filters or conditions that rely on underlying stored data. The database is still comparing the real values under the hood, so these queries work. It’s an expected behavior given DDM’s design. Privileged users: Users who are granted sufficient database permissions, such as the ability to alter table schemas, can directly disable or remove masking. Users with sysadmin, db_owner or CONTROL permission can view unmasked data. Thus, controlling and auditing who holds such privileges is vital. Metadata visibility: Masking rules and associated columns can be discoverable through system metadata. Data movement: Because masking is defined at the schema level in a given database instance, backups or exported datasets may contain unmasked values depending on permissions and configuration. Understanding these design characteristics is important when incorporating DDM into a broader data governance or privacy strategy. Proper use and best practices for DDM Organizations may consider using Dynamic Data Masking in scenarios where consistent display of sensitive values is needed across application(s) or reporting environments. Some implementation considerations include: Using DDM to help standardize how sensitive fields are displayed in query results and reduce developmental effort for data masking Combining DDM with other database or access‑control features as part of a layered data protection strategy Reviewing which users are granted permissions to view unmask data or alter masking configurations. Implementing auditing or monitoring database activity as part of broader governance practices Educating internal stakeholders on how masking operates at the query‑result level Testing masking configurations in non‑production environments prior to deployment Conclusion Dynamic Data Masking can be useful in scenarios where organizations want to manage how sensitive data is displayed in application outputs without modifying stored values. It is designed to operate as part of a broader data access or governance approach rather than as a standalone protection mechanism for stored data. When implemented alongside complementary database features and appropriate access controls, DDM may help support more consistent handling of sensitive values across environments.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. [update 20-mar-2026] Change Event Streaming is now in public preview for Azure SQL Managed instance. Read more here. 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.Zero Trust for data: Make Microsoft Entra authentication for SQL your policy baseline
A policy-driven path from enabled to enforced. Why this matters now Security and compliance programs were once built on an assumption that internal networks were inherently safer. Cloud adoption, remote work, and supply-chain compromise have steadily invalidated that model. U.S. federal guidance has now formalized this shift: Executive Order 14028 calls for modernizing cybersecurity and accelerating Zero Trust adoption, and OMB Memorandum M-22-09 sets a federal Zero Trust strategy with specific objectives and timelines. Meanwhile, attacker economics are changing. Automation and AI make reconnaissance, phishing, and credential abuse cheaper and faster. That concentrates risk on identity—the control plane that sits in front of systems, applications, and data. In Zero Trust, the question is no longer “is the network trusted,” but “is this request verified, governed by policy, and least-privilege?” Why database authentication is a first‑order Zero Trust control Databases are universally treated as crown-jewel infrastructure. Yet many data estates still rely on legacy patterns: password-based SQL authentication, long-lived secrets embedded in apps, and shared administrative accounts that persist because migration feels risky. This is exactly the kind of implicit trust Zero Trust architectures aim to remove. NIST SP 800-207 defines Zero Trust as eliminating implicit trust based solely on network location or ownership and focusing controls on protecting resources. In that model, every new database connection is not “plumbing”—it is an access decision to sensitive data. If the authentication mechanism sits outside the enterprise identity plane, governance becomes fragmented and policy enforcement becomes inconsistent. What changes when SQL uses Microsoft Entra authentication Microsoft Entra authentication enables users and applications to connect to SQL using enterprise identities, instead of usernames and passwords. Across Azure SQL and SQL Server enabled by Azure Arc, Entra-based authentication helps align database access with the same identity controls organizations use elsewhere. The security and compliance outcomes that leaders care about Reduce password and secret risk: move away from static passwords and embedded credentials. Centralize governance: bring database access under the same identity policies, access reviews, and lifecycle controls used across the enterprise. Improve auditability: tie access to enterprise identities and create a consistent control surface for reporting. Enable policy enforcement at scale: move from “configured” controls to “enforced” controls through governance and tooling. This is why Entra authentication is a high-ROI modernization step: it collapses multiple security and operational objectives into one effort (identity modernization) rather than a set of ongoing compensating programs (password rotation programs, bespoke exceptions, and perpetual secret hygiene projects). Why AI makes this a high priority decision AI accelerates both reconnaissance and credential abuse, which concentrates risk on identity. As a result, policy makers increasingly treat phishing-resistant authentication and centralized identity enforcement as foundational—not optional. A practical path: from enabled to enforced Successful security programs define a clear end state, a measurable glide path, and an enforcement model. A pragmatic approach to modernizing SQL access typically includes: Discover active usage: Identify which logins and users are actively connecting and which are no longer required. Establish Entra as the identity authority: Enable Entra authentication on SQL logical servers, starting in mixed mode to reduce disruption. Recreate principals using Entra identities: Replace SQL Authentication logins/users with Entra users, groups, service principals, and managed identities. Modernize application connectivity: Update drivers and connection patterns to use Entra-based authentication and managed identities. Validate, then enforce: Confirm the absence of password‑based SQL authentication traffic, then move to Entra‑only where available and enforce via policy. By adopting this sequencing, organizations can mitigate risks at an early stage and postpone enforcement until the validation process concludes. For a comprehensive migration strategy, refer to Securing Azure SQL Database with Microsoft Entra Password-less Authentication: Migration Guide. Choosing which projects to fund — and which ones to stop When making investment decisions, priority is given to database identity projects that can demonstrate clear risk reduction and lasting security benefits. Microsoft Entra authentication as the default for new SQL workloads, with a defined migration path for the existing workloads. Managed identities for application-to-database connectivity to eliminate stored secrets. Centralized governance for privileged database access using enterprise identity controls. At the same time, organizations should explicitly de-prioritize investments that perpetuate password risk: password rotation projects that preserve SQL Authentication, bespoke scripts maintaining shared logins, and exception processes that do not scale. Security and scale are not competing goals Security is often seen as something that slows down innovation, but database identity offers unique benefits. When enterprise identity is used for access controls, bringing in new applications and users shifts from handing out credentials to overseeing policies. Compliance reporting also becomes uniform rather than customized, making it easier to grow consistently thanks to a single control framework. Modern database authentication is not solely about mitigating risk— it establishes a scalable operational framework for secure data access. A scorecard designed for leadership readiness To elevate the conversation from implementation to governance, use outcome-based metrics: Coverage: Percentage of SQL workloads with Entra authentication enabled. Enforcement: Percentage operating in Entra-only mode after validation. Secret reduction: Applications still relying on stored database passwords. Privilege hygiene: Admin access governed through enterprise identity controls. Audit evidence: Ability to produce identity-backed access reports on demand. These map directly to Zero Trust maturity expectations and provide a defensible definition of “done.” Closing Zero Trust is an operating posture, not a single control. For most organizations, the fastest way to make that posture measurable is to standardize database access on the same identity plane used everywhere else. If you are looking for a single investment that improves security, reduces audit friction, and supports responsible AI adoption, modernizing SQL access with Microsoft Entra authentication — and driving it from enabled to enforced — is one of the most durable choices you can make. References US Government sets forth Zero Trust architecture strategy and requirements (Microsoft Security Blog) Securing Azure SQL Database with Microsoft Entra Password-less Authentication: Migration Guide (Microsoft Tech Community) OMB Memorandum M-22-09: Federal Zero Trust Strategy (White House) NIST SP 800-207: Zero Trust Architecture CISA: Zero Trust Enforce Microsoft Entra-only authentication for Azure SQL Database and Azure SQL Managed InstanceWhy Developers and DBAs love SQL’s Dynamic Data Masking (Series-Part 1)
Dynamic Data Masking (DDM) is one of those SQL features (available in SQL Server, Azure SQL DB, Azure SQL MI, SQL Database in Microsoft Fabric) that both developers and DBAs can rally behind. Why? Because it delivers a simple, built-in way to protect sensitive data—like phone numbers, emails, or IDs—without rewriting application logic or duplicating security rules across layers. With just a single line of T-SQL, you can configure masking directly at the column level, ensuring that non-privileged users see only obfuscated values while privileged users retain full access. This not only streamlines development but also supports compliance with data privacy regulations like GDPR and HIPAA, etc. by minimizing exposure to personally identifiable information (PII). In this first post of our DDM series, we’ll walk through a real-world scenario using the default masking function to show how easy it is to implement and how much development effort it can save. Scenario: Hiding customer phone numbers from support queries Imagine you have a support application where agents can look up customer profiles. They need to know if a phone number exists for the customer but shouldn’t see the actual digits for privacy. In a traditional approach, a developer might implement custom logic in the app (or a SQL view) to replace phone numbers with placeholders like “XXXX” for non-privileged users. This adds complexity and duplicate logic across the app. With DDM’s default masking, the database can handle this automatically. By applying a mask to the phone number column, any query by a non-privileged user will return a generic masked value (e.g. “XXXX”) instead of the real number. The support agent gets the information they need (that a number is on file) without revealing the actual phone number, and the developer writes zero masking code in the app. This not only simplifies the application codebase but also ensures consistent data protection across all query access paths. As Microsoft’s documentation puts it, DDM lets you control how much sensitive data to reveal “with minimal effect on the application layer” – exactly what our scenario achieves. Using the ‘Default’ Mask in T-SQL : The ‘Default’ masking function is the simplest mask: it fully replaces the actual value with a fixed default based on data type. For text data, that default is XXXX. Let’s apply this to our phone number example. The following T-SQL snippet works in Azure SQL Database, Azure SQL MI and SQL Server: SQL -- Step 1: Create the table with a default mask on the Phone column CREATE TABLE SupportCustomers ( CustomerID INT PRIMARY KEY, Name NVARCHAR(100), Phone NVARCHAR(15) MASKED WITH (FUNCTION = 'default()') -- Apply default masking ); GO -- Step 2: Insert sample data INSERT INTO SupportCustomers (CustomerID, Name, Phone) VALUES (1, 'Alice Johnson', '222-555-1234'); GO -- Step 3: Create a non-privileged user (no login for simplicity) CREATE USER SupportAgent WITHOUT LOGIN; GO -- Step 4: Grant SELECT permission on the table to the user GRANT SELECT ON SupportCustomers TO SupportAgent; GO -- Step 5: Execute a SELECT as the non-privileged user EXECUTE AS USER = 'SupportAgent'; SELECT Name, Phone FROM SupportCustomers WHERE CustomerID = 1 Alternatively, you can use Azure Portal to configure masking as shown in the following screenshot: Expected result: The query above would return Alice’s name and a masked phone number. Instead of seeing 222-555-1234, the Phone column would show XXXX. Alice’s actual number remains safely stored in the database, but it’s dynamically obscured for the support agent’s query. Meanwhile, privileged users such as administrator or db_owner which has CONTROL permission on the database or user with proper UNMASK permission would see the real phone number when running the same query. How this helps Developers : By pushing the masking logic down to the database, developers and DBAs avoid writing repetitive masking code in every app or report that touches this data. In our scenario, without DDM you might implement a check in the application like: If user_role == “Support”, then show “XXXX” for phone number, else show full phone. With DDM, such conditional code isn’t needed – the database takes care of it. This means: Less application code to write and maintain for masking Consistent masking everywhere (whether data is accessed via app, report, or ad-hoc query). Quick changes to masking rules in one place if requirements change, without hunting through application code. From a security standpoint, DDM reduces the risk of accidental data exposure and helps in compliance scenarios where personal data must be protected in lower environments or by certain roles, while reducing the developer effort drastically. In the next posts of this series, we’ll explore other masking functions (like Email, Partial, and Random etc) with different scenarios. By the end, you’ll see how each built-in mask can be applied to make data security and compliance more developer-friendly! Reference Links : Dynamic Data Masking - SQL Server | Microsoft Learn Dynamic Data Masking - Azure SQL Database & Azure SQL Managed Instance & Azure Synapse Analytics | Microsoft LearnImproving Azure SQL Database reliability with accelerated database recovery in tempdb
We are pleased to announce that in Azure SQL Database, accelerated database recovery is now enabled in the tempdb database to bring instant transaction rollback and aggressive log truncation for transactions in tempdb. The same improvement is coming to SQL Server and Azure SQL Managed Instance.Azure Data Studio Retirement
We’re announcing the upcoming retirement of Azure Data Studio (ADS) on February 6, 2025, as we focus on delivering a modern, streamlined SQL development experience. ADS will remain supported until February 28, 2026, giving developers ample time to transition. This decision aligns with our commitment to simplifying SQL development by consolidating efforts on Visual Studio Code (VS Code) with the MSSQL extension, a powerful and versatile tool designed for modern developers. Why Retire Azure Data Studio? Azure Data Studio has been an essential tool for SQL developers, but evolving developer needs and the rise of more versatile platforms like VS Code have made it the right time to transition. Here’s why: Focus on innovation VS Code, widely adopted across the developer community, provides a robust platform for delivering advanced features like cutting-edge schema management and improved query execution. Streamlined tools Consolidating SQL development on VS Code eliminates duplication, reduces engineering maintenance overhead, and accelerates feature delivery, ensuring developers have access to the latest innovations. Why Transition to Visual Studio Code? VS Code is the #1 developer tool, trusted by millions worldwide. It is a modern, versatile platform that meets the evolving demands of SQL and application developers. By transitioning, you gain access to cutting-edge tools, seamless workflows, and an expansive ecosystem designed to enhance productivity and innovation. We’re committed to meeting developers where they are, providing a modern SQL development experience within VS Code. Here’s how: Modern development environment VS Code is a lightweight, extensible, and community-supported code editor trusted by millions of developers. It provides: Regular updates. An active extension marketplace. A seamless cross-platform experience for Windows, macOS, and Linux. Comprehensive SQL features With the MSSQL extension in VS Code, you can: Execute queries faster with filtering, sorting, and export options for JSON, Excel, and CSV. Manage schemas visually with Table Designer, Object Explorer, and support for keys, indexes, and constraints. Connect to SQL Server, Azure SQL (all offerings), and SQL database in Fabric using an improved Connection Dialog. Streamline development with scripting, object modifications, and a unified SQL experience. Optimize performance with an enhanced Query Results Pane and execution plans. Integrate with DevOps and CI/CD pipelines using SQL Database Projects. Stay tuned for upcoming features—we’re continuously building new experiences based on feedback from the community. Make sure to follow the MSSQL repository on GitHub to stay updated and contribute to the project! Streamlined workflow VS Code supports cloud-native development, real-time collaboration, and thousands of extensions to enhance your workflows. Transitioning to Visual Studio Code: What You Need to Know We understand that transitioning tools can raise concerns, but moving from Azure Data Studio (ADS) to Visual Studio Code (VS Code) with the MSSQL extension is designed to be straightforward and hassle-free. Here’s why you can feel confident about this transition: No Loss of Functionality If you use ADS to connect to Azure SQL databases, SQL Server, or SQL database in Fabric, you’ll find that the MSSQL extension supports these scenarios seamlessly. Your database projects, queries, and scripts created in ADS are fully compatible with VS Code and can be opened without additional migration steps. Familiar features, enhanced experience VS Code provides advanced tools like improved query execution, modern schema management, and CI/CD integration. Additionally, alternative tools and extensions are available to replace ADS capabilities like SQL Server Agent and Schema Compare. Cross-Platform and extensible Like ADS, VS Code runs on Windows, macOS, and Linux, ensuring a consistent experience across operating systems. Its extensibility allows you to adapt it to your workflow with thousands of extensions. If you have further questions or need detailed guidance, visit the ADS Retirement page. The page includes step-by-step instructions, recommended alternatives, and additional resources. Continued Support With the Azure Data Studio retirement, we’re committed to supporting you during this transition: Documentation: Find detailed guides, tutorials, and FAQs on the ADS Retirement page. Community Support: Engage with the active Visual Studio Code community for tips and solutions. You can also explore forums like Stack Overflow. GitHub Issues: If you encounter any issues, submit a request or report bugs on the MSSQL extension’s GitHub repository. Microsoft Support: For critical issues, reach out to Microsoft Support directly through your account. Transitioning to VS Code opens the door to a more modern and versatile SQL development experience. We encourage you to explore the new possibilities and start your journey today! Conclusion Azure Data Studio has served the SQL community well,but the Azure Data Studio retirement marks an opportunity to embrace the modern capabilities of Visual Studio Code. Transitioning now ensures you’re equipped with cutting-edge tools and a future-ready platform to enhance your SQL development experience. For a detailed guide on ADS retirement , visit aka.ms/ads-retirement. To get started with the MSSQL extension, check out the official documentation. We’re excited to see what you build with VS Code!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.2025 Year in Review: What’s new across SQL Server, Azure SQL and SQL database in Fabric
What a year 2025 has been for SQL! ICYMI and are looking for some hype, might I recommend you start with this blog from Priya Sathy, the product leader for all of SQL at Microsoft: One consistent SQL: The launchpad from legacy to innovation. In this blog post, Priya explains how we have developed and continue to develop one consistent SQL which “unifies your data estate, bringing platform consistency, performance at scale, advanced security, and AI-ready tools together in one seamless experience and creates one home for your SQL workloads in the era of AI.” For the FIFTH(!!) year in a row (my heart is warm with the number, I love SQL and #SQLfamily, and time is flying), I am sharing my annual Year in Review blog with all the SQL Server, Azure SQL and SQL database in Fabric news this year. Of course, you can catch weekly episodes related to what’s new and diving deeper on the Azure SQL YouTube channel at aka.ms/AzureSQLYT. This year, in addition to Data Exposed (52 new episodes and over 70K views!). We saw many new series related to areas like GitHub Copilot, SSMS, VS Code, and Azure SQL Managed Instance land in the channel, in addition to Data Exposed. Microsoft Ignite announcements Of course, if you’re looking for the latest announcements from Microsoft Ignite, Bob Ward and I compiled this slide of highlights. Comprehensive list of 2025 updates You can read this blog (or use AI to reference it later) to get all the updates and references from the year (so much happened at Ignite but before it too!). Here’s all the updates from the year: SQL Server, Arc-enabled SQL Server, and SQL Server on Azure VMs Generally Available SQL Server 2025 is Now Generally Available Backup/Restore capabilities in SQL Server 2025 SQL Server 2025: Deeply Integrated and Feature-rich on Linux Resource Governor for Standard Edition Reimagining Data Excellence: SQL Server 2025 Accelerated by Pure Storage Security Update for SQL Server 2022 RTM CU21 Cumulative Update #22 for SQL Server 2022 RTM Backup/Restore enhancements in SQL Server 2025 Unified configuration and governance Expanding Azure Arc for Hybrid and Multicloud Management US Government Virginia region support I/O Analysis for SQL Server on Azure VMs NVIDIA Nemotron RAG Integration Preview Azure Arc resource discovery in Azure Migrate Multicloud connector support for Google Cloud Migrations Generally Available SQL Server migration in Azure Arc Azure Database Migration Service Hub Experience SQL Server Migration Assistant (SSMA) v10.3, including Db2 SKU recommendation (preview) Database Migration Service: PowerShell, Azure CLI, and Python SDK SQL Server Migration Assistant (SSMA) v10.4, including SQL Server 2025 support, Oracle conversion Copilot Schema migration support in Azure Database Migration Service Preview Azure Arc resource discovery in Azure Migrate Azure SQL Managed Instance Generally Available Next-gen General Purpose Service Tier Improved connectivity types in Azure SQL Managed Instance Improved resiliency with zone redundancy for general purpose, improved log rate for business critical Apply reservation discount for zone redundant Business Critical databases Free offer Windows principals use to simplify migrations Data exfiltration improvements Preview Windows Authentication for Cloud-Native Identities New update policy for Azure SQL Managed Instance Azure SQL Database Generally Available LTR Backup Immutability Free Azure SQL Database Offer updates Move to Hyperscale while preserving existing geo-replication or failover group settings Improve redirect connection type to require only port 1433 and promote to default Bigint support in DATEADD for extended range calculations Restart your database from the Azure portal Replication lag metric Enhanced server audit and server audit action groups Read-access geo-zone redundant storage (RA-GZRS) as a backup storage type for non-Hyperscale Improved cutover experience to Hyperscale SLA-compliant availability metric Use database shrink to reduced allocated space for Hyperscale databases Identify causes of auto-resuming serverless workloads Preview Multiple geo-replicas for Azure SQL Hyperscale Backup immutability for Azure SQL Database LTR backups Updates across SQL Server, Azure SQL and Fabric SQL database Generally Available Regex Support and fuzzy-string matching Geo-replication and Transparent Data Encryption key management Optimized locking v2 Azure SQL hub in the Azure portal UNISTR intrinsic function and ANSI SQL concatenation operator (||) New vector data type JSON index JSON data type and aggregates Preview Stream data to Azure Event Hubs with Change Event Streaming (Azure SQL DB Public Preview/Fabric SQL Private Preview) DiskANN vector indexing SQL database in Microsoft Fabric and Mirroring Generally Available Fabric Databases SQL database in Fabric Unlocking Enterprise ready SQL database in Microsoft Fabric: ALM improvements, Backup customizations and retention, Copilot enhancements & more update details Mirroring for SQL Server Mirroring for Azure SQL Managed Instance in Microsoft Fabric Connect to your SQL database in Fabric using Python Notebook Updates to database development tools for SQL database in Fabric Using Fast Copy for data ingestion Copilot for SQL analytics endpoint Any updates across Microsoft Fabric that apply to the SQL analytics endpoint are generally supported in mirrored databases and Fabric SQL databases via the SQL analytics endpoint. This includes many exciting areas, like Data Agents. See the Fabric blog to get inspired Preview Data virtualization support Workspace level Private Link support (Private Preview) Customer-managed keys in Fabric SQL Database Auditing for Fabric SQL Database Fabric CLI: Create a SQL database in Fabric SQL database workload in Fabric with Terraform Spark Connector for SQL databases Tools and developer Blog to Read: How the Microsoft SQL team is investing in SQL tools and experiences SQL Server Management Studio (SSMS) 22.1 GitHub Copilot Walkthrough (Preview): Guided onboarding from the Copilot badge. Copilot right-click actions (Preview): Document, Explain, Fix, and Optimize. Bring your own model (BYOM) support in Copilot (Preview). Copilot performance: improved response time after the first prompt in a thread. Fixes: addressed Copilot “Run ValidateGeneratedTSQL” loop and other stability issues. SQL Server Management Studio (SSMS) 22 Support for SQL Server 2025 Modern connection dialog as default + Fabric browsing on the Browse tab. Windows Arm64 support (initial) for core scenarios (connect + query). GitHub Copilot in SSMS (Preview) is available via the AI Assistance workload in the VS Installer. T-SQL/UX improvements: open execution plan in new tab, JSON viewer, results grid zooms. New index support: create JSON and Vector indexes from Object Explorer SQL Server Management Studio (SSMS) 21 Installation and automatic updates via Visual Studio Installer. Workloads/components model: smaller footprint + customizable install. Git integration is available via the Code tools workload. Modern connection dialog experience (Preview). New customization options (e.g., vertical tabs, tab coloring, results in grid NULL styling). Always Encrypted Assessment in the Always Encrypted Wizard. Migration assistance via the Hybrid and Migration workload. mssql-python Driver ODBC: Microsoft ODBC Driver 18.5.2.1 for SQL Server OLE DB: Microsoft OLE DB Driver 19.4.1 for SQL Server JDBC (latest train): Microsoft JDBC Driver for SQL Server 13.2.1 Also updated in 2025: supported JDBC branches received multiple servicing updates (including Oct 13, 2025, security fixes). See the same JDBC release notes for the full list. .NET: Microsoft.Data.SqlClient 6.0.2 Related - some notes on drivers released/updated in 2025 (recap): MSSQL extension for VS Code 1.37.0 GitHub Copilot integration : Ask/Agent modes, slash commands, onboarding. Edit Data : interactive grid for editing table data (requires mssql.enableExperimentalFeatures: true). Data-tier Application dialog : deploy/extract .dacpac and import/export .bacpac (requires mssql.enableExperimentalFeatures: true). Publish SQL Project dialog : deploy .sqlproj to an existing DB or a local SQL dev container. Added “What’s New” panel + improved query results grid stability/accessibility. MSSQL extension for VS Code 1.36.0 Fabric connectivity : browse Fabric workspaces and connect to SQL DBs / SQL analytics endpoints. SQL database in Fabric provisioning : create Fabric SQL databases from Deployments. GitHub Copilot slash commands : connection, schema exploration, query tasks. Schema Compare extensibility: new run command for external extensions/SQL Projects (incl. Update Project from Database support). Query results in performance/reliability improvements (incremental streaming, fewer freezes, better settings handling). SqlPackage 170.0.94 release notes (April 2025) Vector: support for vector data type in Azure SQL Database target platform (import/export/extract/deploy/build). SQL projects: default compatibility level for Azure SQL Database and SQL database in Fabric set to 170. Parquet: expanded supported types (including json, xml, and vector) + bcp fallback for unsupported types. Extract: unpack a .dacpac to a folder via /Action:Extract. Platform: Remove .NET 6 support; .NET Framework build updated to 4.7.2. SqlPackage 170.1.61 release notes (July 2025) Data virtualization (Azure SQL DB): added support for data virtualization objects in import/export/extract/publish. Deployment: new publishing properties /p:IgnorePreDeployScript and /p:IgnorePostDeployScript. Permissions: support for ALTER ANY EXTERNAL MIRROR (Azure SQL DB + SQL database in Fabric) for exporting mirrored tables. SQL Server 2025 permissions: support for CREATE ANY EXTERNAL MODEL, ALTER ANY EXTERNAL MODEL, and ALTER ANY INFORMATION PROTECTION. Fixes: improved Fabric compatibility (e.g., avoid deploying unsupported server objects; fixes for Fabric extraction scripting). SqlPackage 170.2.70 release notes (October 2025) External models: support for external models in Azure SQL Database and SQL Server 2025. AI functions: support for AI_GENERATE_CHUNKS and AI_GENERATE_EMBEDDINGS. JSON: support for JSON indexes + functions JSON_ARRAYAGG, JSON_OBJECTAGG, JSON_QUERY. Vector: vector indexes + VECTOR_SEARCH and expanded vector support for SQL Server 2025. Regex: support for REGEXP_LIKE. Microsoft.Build.Sql 1.0.0 (SQL database projects SDK) Breaking: .NET 8 SDK required for dotnet build (Visual Studio build unchanged). Globalization support. Improved SDK/Templates docs (more detailed README + release notes links). Code analyzer template defaults DevelopmentDependency. Build validation: check for duplicate build items. Microsoft.Build.Sql 2.0.0 (SQL database projects SDK) Added SQL Server 2025 target platform (Sql170DatabaseSchemaProvider). Updated DacFx version to 170.2.70. .NET SDK targets imported by default (includes newer .NET build features/fixes; avoids full rebuilds with no changes Azure Data Studio retirement announcement (retirement February 28, 2026) Anna’s Pick of the Month Year It’s hard to pick a highlight representative of the whole year, so I’ll take the cheesy way out: people. I get to work with great people working on a great set of products for great people (like you) solving real world problems for people. So, thank YOU and you’re my pick of the year 🧀 Until next time… That’s it for now! We release new episodes on Thursdays and new #MVPTuesday episodes on the last Tuesday of every month at aka.ms/azuresqlyt. The team has been producing a lot more video content outside of Data Exposed, which you can find at that link too! Having trouble keeping up? Be sure to follow us on twitter to get the latest updates on everything, @AzureSQL. And if you lose this blog, just remember aka.ms/newsupdate2025 We hope to see you next YEAR, on Data Exposed! --Anna and MarisaIntroducing "Backups on Secondary" for SQL Server Always On Availability Groups with SQL Server 2025
We’re excited to announce a major enhancement for SQL Server Always On Availability Groups Backups on Secondary, in SQL Server 2025. Until SQL Server 2022, you could only perform COPY_ONLY full backups and transaction log backups on a secondary replica of an Always On Availability Group. This enhancement in SQL Server 2025 allows you to offload all types of backups—full, differential, and transaction logs - to a secondary replica, significantly improving performance, resource utilization, and operational flexibility. What Is "Backups on Secondary"? Traditionally, backups in an Always On Availability Group were typically performed on the primary replica, which could lead to resource contention and performance degradation for mission-critical workloads. With this update in SQL Server 2025 to the Backups on Secondary feature, you can now configure your environment to perform all backup operations on a designated secondary replica. This includes: Full backups Differential backups Transaction log backups Key Benefits 🔄 Reduced Load on Primary Replica By offloading backup operations to a secondary replica, you free up CPU, memory, and I/O resources on the primary replica, ensuring that your production workloads run more smoothly and efficiently. 🧩 Flexible Backup Strategies You can now design more flexible and resilient backup strategies by leveraging secondary replicas in different geographic locations or data centers. 🛡️ Enhanced High Availability and Disaster Recovery In the event of a failover, backup operations can continue seamlessly on another secondary replica, ensuring continuous data protection. How It Works The feature is easy to configure using T-SQL or SQL Server Management Studio (SSMS). There are two steps to make backups run on secondary: Configure backups to run on secondary replica via the AUTOMATED_BACKUP_PREFERENCE and BACKUP_PRIORITY parameters – refer to Configure backups on secondary replicas of an Always On availability group for prerequisites and detailed steps. Run the T-SQL BACKUP command using one of the tools such as SSMS or SQL Agent jobs or Maintenance plans. These are the same steps that have been currently in place for SQL Server 2022 and for prior versions. In the prior versions this configuration only allowed COPY_ONLY and transaction log backups. Now, starting with SQL Server 2025, with the same configuration in place, you can perform FULL, DIFFERENTIAL and T-LOG backups on a secondary replica and truly offload the backup overhead on to the secondary replica. You can also query system views like sys.dm_hadr_backup_is_preferred_replica to programmatically determine the preferred replica for backups. Final Thoughts The Backups on Secondary feature is a game-changer for organizations looking to optimize their SQL Server environments for performance, availability, and cost. Whether you're managing a large-scale enterprise deployment or a hybrid cloud setup, this feature gives you the flexibility and control you need to build a more efficient and resilient data platform. This feature is available in SQL Server 2025 CTP 2.0. You can download it and try it out today.Whats new in the Backup/Restore area 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. Following are three “hidden gems” (as one of our customers called them), in the Backup/Restore area we announced with SQL Server 2025. 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. 1. 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. 2. 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. 3. 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!
