Public Preview - Data Virtualization for Azure SQL Database
Data virtualization, now in public preview in Azure SQL Database, enables you to leverage all the power of Transact-SQL (T-SQL) and seamlessly query external data from Azure Data Lake Storage Gen2 or Azure Blob Storage, eliminating the need for data duplication, or ETL processes, allowing for faster analysis and insights. Integrate external data, such as CSV, Parquet, or Delta files, with your relational database while maintaining the original data format and avoiding unnecessary data movement. Present integrated data to applications and reports as a standard SQL object or through a normal SELECT command. Data Virtualization for Azure SQL Database supports SAS tokens, Managed Identity, and User identity for secure access. Data Virtualization for Azure SQL Database will introduce and expand support for: Database Scoped Credential. External Data Source. External File Format - with support for Parquet, CSV, and Delta. External Tables. OPENROWSET. Support metadata functions and JSON functions. For enhanced security and flexibility Data Virtualization for Azure SQL Database supports three authentication methods: Shared access signature. Managed identity (system assigned managed identity and user-assigned managed identity). User identity. Key Benefits Just like in SQL Server 2022 and Azure SQL Managed Instance the key benefits of Data Virtualization for Azure SQL Database are: Seamless Data Access: Query external CSV, Parquet, and Delta Lake tables using T-SQL as if they were native tables within Azure SQL Database. Allowing for off-loading cold data while keeping it easily accessible. Enhanced Productivity: Reduce the time and effort required to integrate and analyze data from multiple sources. Cost Efficiency: Minimize the need for data replication and storage costs associated with traditional data integration methods. Real-Time Insights: Enable real-time data querying and insights without delays caused by data movement or synchronization. Security: Leverage SQL Server security features for granular permissions, credential management, and control. Example Data Virtualization for Azure SQL Database is based on the same core principles as SQL Server’s PolyBase feature. With support for Azure Data Lake Gen 2, using prefix adls:// and Azure Blob Storage, using prefix abs://. For the following example we are going to use Azure Open Datasets, more specifically NYC yellow taxi trip records open data set which allows public access. For private data sources customers can leverage multiple authentication methods like SAS Tokens, Managed Identity and User Identity. -- Create Azure Blob Storage (ABS) data source CREATE EXTERNAL DATA SOURCE NYCTaxiExternalDataSource WITH ( LOCATION = 'abs://nyctlc@azureopendatastorage.blob.core.windows.net'); -- Using OPENROWSET to read Parquet files from the external data source SELECT TOP 10 * FROM OPENROWSET( BULK '/yellow/puYear=*/puMonth=*/*.parquet', DATA_SOURCE = 'NYCTaxiExternalDataSource', FORMAT = 'parquet' ) AS filerows; -- Or using External Tables CREATE EXTERNAL FILE FORMAT DemoFileFormat WITH (FORMAT_TYPE = PARQUET); --Create external table CREATE EXTERNAL TABLE tbl_TaxiRides ( vendorID VARCHAR(100) COLLATE Latin1_General_BIN2, tpepPickupDateTime DATETIME2, tpepDropoffDateTime DATETIME2, passengerCount INT, tripDistance FLOAT, puLocationId VARCHAR(8000), doLocationId VARCHAR(8000), startLon FLOAT, startLat FLOAT, endLon FLOAT, endLat FLOAT, rateCodeId SMALLINT, storeAndFwdFlag VARCHAR(8000), paymentType VARCHAR(8000), fareAmount FLOAT, extra FLOAT, mtaTax FLOAT, improvementSurcharge VARCHAR(8000), tipAmount FLOAT, tollsAmount FLOAT, totalAmount FLOAT ) WITH ( LOCATION = 'yellow/puYear=*/puMonth=*/*.parquet', DATA_SOURCE = NYCTaxiExternalDataSource, FILE_FORMAT = DemoFileFormat ); SELECT TOP 10 * FROM tbl_TaxiRides; You can also use these capabilities in combination with other metadata functions like sp_describe_first_result_set, filename(), and filepath(). Getting started Data Virtualization for Azure SQL Database is currently available in select regions, with broader availability coming soon across all Azure regions Data Virtualization for Azure SQL Database is based on the same core principles as SQL Server’s PolyBase feature. To know more and get started with Data Virtualization for Azure SQL Database.Automatic Connectivity Tests for Azure SQL Managed Instance
To further enhance connectivity monitoring and improve service reliability, we’re introducing automatic internal connectivity tests for all Azure SQL Managed Instances. These tests are fully automated and require no action from you. Beginning May 2026, the tests will be continuously performed at regular intervals on all managed instances. By proactively monitoring internal network connections, we’re able to quickly identify potential issues and maintain stable end-to-end connectivity. These tests are performed from a pair of internal IP addresses from the subnet range that hosts the managed instance, so they do not require any external inbound or outbound connectivity. Please note that additional IP addresses will be reserved for these tests and that tests may leave traces in your observability logs. Automatic tests diagnose issues in internal service and network availability. This results in accelerated issue discovery and shorter time to mitigate incidents that involve degraded connectivity of managed instances’ internal networking components. This suite of connectivity tests examines internal network connections at several levels, boosting the supportability and visibility into the service’s internal state and offering you peace of mind regarding your managed instances. Do note that your audit and security systems, if configured to track certain types of events emitted by SQL Server, may record failed login attempts. Those are normal and expected byproducts of the end-to-end connectivity test suite. If you would prefer to not have those events register in your SQL Server audit logs, SQL error logs, or captured Extended Events, we provide you with their event signatures so you can set up event filters or configure your SIEM system to ignore them: Observing failed logins caused by end-to-end tests. You can read more about the automated connectivity tests at Automatic internal connectivity tests for Azure SQL Managed Instance.
Replication lag metric for Azure SQL DB is now in public preview
Azure SQL Database offers business continuity capabilities to recover quickly from regional disasters. Features such as active geo-replication and failover groups provide continuous replication of the data in your primary database to a secondary database in a different Azure region. In the event of a regional disruption, these features allow you to perform quick disaster recovery to your secondary database to meet your business' recovery time objective (RTO) and recovery point objective (RPO). RTO for Azure SQL Database is typically less than 60 seconds, but RPO depends on the amount of data changes before the disruptive event that have not been replicated. Consequently, monitoring the replication lag between the primary and secondary databases is critical in ensuring your RPO goals are met. Until now, the main way to measure the replication lag between the primary and secondary databases was with the replication_lag_sec column of the dynamic management view (DMV), sys.dm_geo_replication_link_status from your primary database. With the introduction of the Replication lag metric, you can now monitor lag with respect to RPO in near real time in the Azure portal in addition to using the DMV. Replication lag is a new Azure monitor metric that is emitted at a one-minute frequency and stored up to 93 days. You can visualize the metric in Azure monitor and set up alerts too. The replication lag metric measures the time span in seconds from the point of transaction commit on the primary database and acknowledgement by the secondary database that the transaction log update has been persisted. The replication lag metric is applicable for a database in DTU or vCore purchasing model and in all service tiers (Basic, Standard, Premium, General Purpose, Business Critical & Hyperscale). Both singleton and elastic pool deployments are supported. You can monitor the metric by adding Replication lag (preview) from your primary database in the portal as shown below: The metric provides three dimensions, Partner Database Name, Partner Server Name, and Partner Resource ID that you can use to further filter or split the data to view specific replication links. If your database is configured to send Metrics to Log Analytics under “Diagnostic settings”, you can also query the Replication lag metric data as shown below: Next Steps Learn more about monitoring geo-replication and other commonly used metrics in Azure SQL Database. See how you can achieve your business continuity goals with Azure SQL Database using Active geo-replication and Failover groups. Prepare for disasters with the Disaster recovery checklist Frequently Asked Questions What is the Replication lag and what does it measure? The Replication lag is a new metric in Azure monitor that measures the time span in seconds between the transactions committed on the primary and hardened to the transaction log on the secondary. How do you view the metric in the portal? In the Azure portal, select your primary SQL database and under "Monitoring", select "Metrics". In the "Metrics" dropdown, choose "Replication lag (preview)". What is the granularity of the Replication lag metric? One minute. What is the latency in displaying the Replication lag data in the Metrics screen? Typically, the latency to display the replication lag is less than three minutes. Are there dimensions available for the Replication lag metric? Yes, there are three dimensions available for the metric - Partner Database Name, Partner Server Name, and Partner Resource ID. These dimensions can be used for filtering and splitting the view in the Metrics screen for easier comparison of multiple secondary geo-replicas.Unlocking More Power with Flexible Memory in Azure SQL Managed Instance
Service update - May 6th 2026. Next-gen General Purpose: locally redundant instances. Flexible memory for the Next-gen General purpose tier is generally available (GA). Business Critical: locally redundant and zone-redundant instances. Flexible memory is currently in preview for the Business Critical service tier. As data workloads grow in complexity and scale, so does the need for more adaptable and performant database infrastructure. That’s why we’re excited to introduce a new capability in Azure SQL Managed Instance: Flexible Memory, now generally available. What Is Flexible Memory? Flexible Memory allows you to customize the memory-to-vCore ratio in your SQL Managed Instance, enabling finer control over both performance and cost based on your workload requirements. This capability is part of the next-generation General Purpose and Business Critical tiers. It introduces a memory slider, which enables you to scale memory independently within supported limits - without changing the number of vCores. The memory slider is currently available only on premium-series hardware. Why It Matters Traditionally, memory allocation in SQL Managed Instance was fixed per vCore. With Flexible Memory, you can now: Increase memory beyond the default allocation Optimize for memory-intensive workloads without overprovisioning compute Pay only for what you use - additional memory is billed per GB/hour This flexibility is especially valuable for scenarios like analytics, caching, or workloads with large buffer pool requirements. How It Works Memory scales based on the number of vCores and the selected hardware tier: Hardware Tier Memory per vCore (GB) Standard-series 5.1 Premium series 7–12 Premium series (memory-optimized) Up to 13.6 You can select from predefined memory ratios (e.g., 7, 8, 10, 12 GB per vCore) depending on your configuration. For example, a 10 vCore instance can be configured with 70 GB to 120 GB of memory. One of the most powerful aspects of the Flexible Memory feature is the ability to select from a range of memory-to-vCore ratios. These “click stops” allow you to tailor memory allocation precisely to your workload’s needs - whether you’re optimizing for performance, cost, or both. The table below outlines the available configurations for Premium Series hardware, showing how memory scales across 16 vCore sizes: vCores Available Ratios Total Memory Options (GB) 4 7, 8, 10, 12 28, 32, 40, 48 6 7, 8, 10, 12 42, 48, 60, 72 8 7, 8, 10, 12 56, 64, 80, 96 10 7, 8, 10, 12 70, 80, 100, 120 12 7, 8, 10, 12 84, 96, 120, 144 16 7, 8, 10, 12 112, 128, 160, 192 20 7, 8, 10, 12 140, 160, 200, 240 24 7, 8, 10, 12 168, 192, 240, 288 32 7, 8, 10, 12 224, 256, 320, 384 40 7, 8, 10, 12 280, 320, 400, 480 48 7, 8, 10 336, 384, 480 56 7, 8 392, 448 64 7 448 80 7 560 96 5.83 560 128 4.38 560 Pricing model Flexible Memory introduces a usage-based pricing model that ensures you only pay for the memory you actually consume beyond the default allocation. This model is designed to give you the flexibility to scale memory without overcommitting on compute resources - and without paying for unused capacity. How it works: Default memory is calculated based on the minimum memory-to-vCore ratio Billable memory is the difference between your configured memory and the default allocation. Billing is per GB/hour, so you’re charged only for the additional memory used over time. Let’s take an example of SQL Managed Instance running on premium series hardware with 4 vCores and 40GB of memory. Configuration Value vCores 4 Configured Memory 40 GB Default Memory (4 × 7 GB) 28 GB Billable Memory 12 GB Billing Unit Per GB/hour Charged For 12 GB of additional memory Management Experience Changing memory behaves just like changing vCores: Seamless updates via Azure Portal, PowerShell, SDK or API Failover group guidance remains the same Upgrade secondary first Configurations between primary and secondary should match Adjusting the memory is fully online operation, with a short failover at the very end of it. The operation will go through the process of allocating the new compute with specified configuration, which takes approximately 60 minutes, with new faster management operations. API Support Flexible Memory is fully supported via API (the minimal API version that can be used is 2024-08-01) and Azure Portal. Here’s a sample API snippet to configure memory: { "properties": { "memorySizeInGB": 96 } } Portal support Note: Portal experience is in the rollout for Business Critical service tier! Use API, PowerShell or Azure CLI for configuring additional memory for your Business Critical instance. Summary The new Flexible Memory capability in Azure SQL Managed Instance empowers you to scale memory independently of compute, offering greater control over performance and cost. With customizable memory-to-vCore ratios, a transparent pricing model, and seamless integration into existing management workflows, this feature is ideal for memory-intensive workloads and dynamic scaling scenarios. Whether you're optimizing for analytics, caching, or simply want more headroom without overprovisioning vCores, Flexible Memory gives you the tools to do it - efficiently and affordably. Next Steps Review the Documentation: Explore detailed configuration options, supported tiers, and API usage. Additional memory Management operations overview Management operations duration Test Your Workloads: Use the memory slider in the Azure Portal, PowerShell, SDK or API to experiment with different configurations. Learn more What is Azure SQL Managed Instance Try Azure SQL Managed Instance for free Next-gen General Purpose – official documentation Analyzing the Economic Benefits of Microsoft Azure SQL Managed Instance How 3 customers are driving change with migration to Azure SQL Accelerate SQL Server Migration to Azure with Azure ArcAzure SQL is Retiring the “No Minimum TLS” (MinTLS None) Configuration
As part of the retirement of lower TLS versions 1.0 and 1.1 and the enforcement of 1.2 as the new default minimum TLS version, we will be removing the No Minimum TLS (MinTLS = “None” or "0") option and updating these configurations to TLS 1.2. No Minimum TLS allowed Azure SQL Database and Azure SQL Managed Instance resources to accept client connections using any TLS protocol version and unencrypted connections. Over the past year, Azure has retired TLS 1.0 and 1.1 for all Azure databases, due to known security vulnerabilities in these older protocols. As of August 31, 2025, creating servers configured with versions 1.0 and 1.1 was disallowed and migration to 1.2 began. With legacy TLS versions being retired, TLS 1.2 will become the secure default minimum TLS version for new Azure SQL DB and MI configurations and for all client-server connections, rendering the MinTLS = None setting obsolete. As a result, the MinTLS = None configuration option will be retired for new servers, and existing servers configured with No Minimum TLS will be upgraded to 1.2. What is changing? After July 31, 2026, we will disallow minimum TLS value "None", for the creation of new SQL DB and MI resources using PowerShell, Azure CLI, and any other REST based interface. This configuration option has already been removed from the Portal as part of the retirement of TLS versions 1.0 and 1.1. Creating new Azure SQL Database and Managed Instance servers with MinTLS = None (which was previously considered the default) will no longer be a supported configuration. If the server parameter value for the minimum TLS is left blank, it will default to minimum TLS version 1.2. Attempts to create an Azure SQL server with MinTLS = None will fail with an “Invalid operation” error and downgrades to None will be disallowed. While attempts to connect with TLS 1.0, 1.1 or unencrypted connections will fail with “Error: 47072/171 on Gateway.” Effective date (retirement milestone) MinTLS = None (0) MinTLS left blank (defaults to supported minimum) Before 8/31/25 Any + Unencrypted Any + Unencrypted After 8/31/25 1.2 + Unencrypted 1.2 After July 31, 2026 Invalid operation error (for new server creates) Downgrades will be disallowed TLS error: 47072/171 (for unencrypted connections) 1.2 In summary, after July 31, 2026, Azure SQL Database and Azure SQL Managed Instance will require all client connections to use TLS 1.2 or higher and unencrypted connections will be denied. The minimum TLS version setting will no longer accept the value "None" for new or existing servers and servers currently configured with this value will be upgraded to explicitly enforce TLS 1.2. Who is impacted? For most Azure SQL customers, there is no action required. Most clients already use TLS 1.2 or higher. After July 31, 2026, if your Azure SQL Database or Managed Instance is still configured with No Minimum TLS and using 1.0, 1.1 or unencrypted connections, it will automatically update to TLS 1.2 to reflect the current minimum protocol enforcement in client-server connectivity. We do recommend you verify your client applications – especially any older or third-party client drivers – to ensure they can communicate with TLS 1.2 or above. In some rare cases, very old applications, such as an outdated JDBC driver or older .NET framework version, may need an update or need to enable TLS 1.2. Conclusion This retirement is part of Azure’s broader security strategy to ensure encrypted connections are secure by modern encryption standards. TLS version 1.2 is more secure than older versions and is now the industry standard (required by regulations like PCI DSS and HIPAA). This change eliminates the use of unencrypted connections which ensure all database connections meet current security standards. If you’ve already migrated to TLS 1.2 (as most customers have), you will most likely not notice any change, except that the No Minimum TLS option will disappear from configurations.Database DevOps (preview) in SSMS 22.4.1
Database DevOps tooling for Microsoft SQL brings the benefits of database-as-code to your development workflow. At its core are SQL database projects, which enable you to source control your database schema, perform reliable deployments to any environment, and integrate code quality checks into your development process.
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.Announcing Public Preview: Auditing for Fabric SQL Database
We’re excited to announce the public preview of Auditing for Fabric SQL Database—a powerful feature designed to help organizations strengthen security, ensure compliance, and gain deep operational insights into their data environments. Why Auditing Matters Auditing is a cornerstone of data governance. With Fabric SQL Database auditing, you can now easily track and log database activities—answering critical questions like who accessed what data, when, and how. This supports compliance requirements (such as HIPAA and SOX), enables robust threat detection, and provides a foundation for forensic investigations. Key Highlights Flexible Configuration: Choose from default “audit everything,” preconfigured scenarios (like permission changes, login attempts, data reads/writes, schema changes), or define custom action groups and predicate filters for advanced needs. Seamless Access: Audit logs are stored in One Lake, making them easily accessible via T-SQL or One Lake Explorer. Role-Based Access Control: Configuration and log access are governed by both Fabric workspace roles and SQL-level permissions, ensuring only authorized users can view or manage audit data. Retention Settings: Customize how long audit logs are retained to meet your organization’s policy. How It Works Audit logs are written to a secure, read-only folder in One Lake and can be queried using the sys. fn_get_audit_file_v2 T-SQL function. Workspace and artifact IDs are used as identifiers, ensuring logs remain consistent even if databases move across logical servers. Access controls at both the workspace and SQL database level ensure only the right people can configure or view audit logs. Example Use Cases Compliance Monitoring: Validate a full audit trail for regulatory requirements. Security Investigations: Track specific events like permission changes or failed login attempts. Operational Insights: Focus on specific operations (e.g., DML only) or test retention policies. Role-Based Access: Verify audit visibility across different user roles. Getting Started You can configure auditing directly from the Manage SQL Auditing blade in the Fabric Portal. Choose your preferred scenario, set retention, and (optionally) define custom filters—all through a simple, intuitive interface. Learn more about auditing for Fabric SQL database here Data exposed session with demo hereZero 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 Instance
