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

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

AI built-in

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

Build AI applications with your enterprise database

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

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

Boost developer productivity

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

Best-in-class security and performance

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

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

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

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

Microsoft Fabric and Azure Arc connected

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

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

Sign up for the preview today

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

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

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

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

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

SQL Server Management Studio 18.11

SSMS 18.11 adds support for creating ledger tables, viewing the history table, and scripting support for ledger objects in Azure SQL Database, as well as the capability to review trace files from Analysis Services (version 16.0) in Profiler. This version also includes the fix to use CTRL+mousewheel to zoom in query plans, and the ability to edit and create SQL Server Agent jobs in Managed Instance. Additional improvements related to accessibility and underlying code were implemented, as well as expanded support for special characters in connection names, and minor UI fixes.  

Azure Data Studio 1.35

We are excited to announce the release of Azure Data Studio 1.35. This release is focused on bringing new innovations to the core SQL Server data toolset with a focus on data scheming using a modern table designer and query performance tuning.

Table Designer (preview) in Azure Data Studio

Inspired by SQL Server Data Tools (SSDT), the Table Designer provides the ability to manage table metadata, view detailed properties, and preview generated SQL DDL. Supported table metadata includes columns, foreign keys, indexes, and check constraints definitions. The new Table Designer preview feature supports creating new tables and editing existing tables on a connected SQL Server instance. This is a highly requested product enhancement and enables more productive schema management with a modern, streamlined UX. This feature is built on the DacFx framework, which is the same mature library that is used in the SSDT Table Designer.

Query Plan Viewer (preview) in Azure Data Studio

We are excited to introduce the Query Plan Viewer for Azure Data Studio. 

Both estimated and actual query plans can be displayed, and no extension is required. The initial release of the plan viewer includes components familiar to users who analyze plans, as well as new functionality including Top Operations and the ability to open the query in a new window, directly from the plan.

The plan viewer allows users to understand object access and join type for a query, view operator properties to identify what consumes the most time or CPU, as well as save plans for additional analysis.  Future improvements will be added; look for opportunities to provide feedback about what you would like to see next!

Notebooks in Azure Data Studio

Azure Data Studio 1.35 now supports easier keyboard navigation in notebooks without mouse clicking. This is done by hitting the Esc key and navigating between cell rows using the Up and Down arrow keys. To enter edit mode, hit the Enter key on the keyboard.

Azure SQL Migration extension update

We made some major updates in the Azure SQL Migration extension. These include provision of recommendations for right-sized Azure SQL Managed Instance SKUs or SQL Server on Azure Virtual Machines SKUs based on performance characteristics of your on-premises SQL Server as well as an ability to provide performance database assessments and recommendations without the need for an Azure Account. To learn more, read the Azure SQL Migration extension article on Tech Community.

DacFx 160 and SqlPackage 19 general availability

As mentioned in a prior post on the Data-Tier Application Framework (DacFx), DacFx 160 is the latest iteration of the .NET library for database development and CI/CD operations for Microsoft SQL Server and Azure SQL. DacFx supports declarative development with the capability to apply database changes based on the current state of a database and the contents of a dacpac or SQL project. In addition to being available as a .NET package on NuGet as well as in the SqlPackage CLI, DacFx is available in both Azure DevOps pipelines and GitHub actions. Using DacFx on these automation platforms can be achieved through published tasks or actions and by directly invoking SqlPackage in the workflow. With each DacFx release, the provided virtual environments are automatically updated ensuring that your SQL deployment workflows receive the latest version.

In January 2022, DacFx 160 became generally available. With this version of DacFx, the dependency on System.Data.SqlClient is replaced with Microsoft.Data.SqlClient. Additionally, support is added for .NET 6 and column-level symmetric encryption in Azure Synapse Analytics. Information on these and other changes is available in the SqlPackage release notes, and users can download the latest SqlPackage and join the conversations on DacFx at the GitHub repository.

Contact us

If you have any feature requests or issues, please submit them to our GitHub issues page. For any questions, feel free to comment below or tweet us @AzureDataStudio.

The post Introducing new and improved updates to SQL tools on Azure appeared first on Microsoft SQL Server Blog.

As the lead sponsor, we will deliver content including the keynote, five full-day training days, and over fifty general sessions. With so many opportunities to educate, we are bringing the full Azure data team including folks from across the data platform, such as SQL Server, Azure SQL, Cosmos DB, Azure Purview, Azure Synapse Analytics, and Power BI.

Start the week with my team for two day-long training sessions where you will have a unique chance to work directly with Microsoft engineering:

The Hands-on Azure SQL Workshop on March 8 will help you translate your existing SQL Server skills to Azure SQL. Bring your laptop and get ready to learn hands-on. You will gain a foundational knowledge of what to use when, as well as how to configure, monitor, and troubleshoot the “meat and potatoes” of SQL Server in Azure: security, performance, and availability.

Migrate SQL Server to Azure on Wednesday, March 9 will help you migrate your SQL Server environments to Azure. In this session, the Microsoft engineering team will show you everything you need to know, including the tools and knowledge you need to make your migrations seamless, cost-efficient, and optimized for speed.

Other training sessions cover topics such as Azure SQL Database, Synapse Analytics, and Power BI.

All speaker proceeds from these sessions will be given back to a local charity.

The SQLBits event theme this year is Video Games—and in the “Level Up With Azure Data” keynote, Buck Woody has asked me to come talk about SQL Server 2022 and Azure Data. He assures me I will have help with some surprise guests so it should be interesting. It is always a fun keynote when Buck and I are on stage, and this year you really do not want to miss it!

You also have the opportunity to attend the Microsoft general sessions to learn about the entire Azure data platform.

Take a look at some of the learning available SQLBits 2022

See all the opportunities to engage with Microsoft engineering by heading over to our blog on Microsoft Tech Community, Ready for SQLBits 2022. And don’t forget to stop by our booth, where you can get your questions answered by members of the Microsoft team.

SQLBits is a marathon of top-quality training from global specialists, with two days of full-day training sessions and three days of general sessions. As always with SQLBits, Saturday, March 12 is free to attend. Meet with community leaders sharing their real-world experience and Microsoft product teams providing deep insights into innovations that meet your needs.

Register today for SQLBits 2022

Join Microsoft at this hybrid event for the latest on the data platform and a chance to see whether Buck Woody or I have the best arcade game skills!

Register to attend, and we’ll see you there, in-person, or virtually!

The post Meet us at SQLBits 2022 and level up as a data professional appeared first on Microsoft SQL Server Blog.

Deliver faster performance than ever before with SQL Server and Azure

Hear directly from Microsoft’s Rohan Kumar and senior Microsoft engineering leaders during the day one kick-off keynote as they take you on a journey to a new universe shaped by our past—and built to take us into a limitless future. The cloud has created a whole new universe and advancements in Microsoft data products and services are your bridge.

You’ll see how you can use your existing SQL Server and Azure skills, and learn about new tools and platforms available from Microsoft to deliver faster performance than ever before. You’ll see how to shape your data so you can harness its power to find a new galaxy of insights, answers, and predictions. And you will hear about new innovations that continue Microsoft’s rich heritage of data integrity and governance.

Additionally, in the special on-demand keynote, Microsoft Azure Data CTO Raghu Ramakrishnan and team will share a technical keynote and demos showing Azure Purview and SQL.

Register for the PASS Data Community Summit

Don’t miss this opportunity to see how Microsoft is uniquely positioned to provide you with an end-to-end data platform seamlessly integrating limitless database scale and performance, unmatched analytics and intelligence, and unified data governance.

After the keynotes, ground your learning with in-depth training in one of more than two dozen sessions Microsoft will be delivering. Hear the latest from the Engineering teams who develop the tools you use every day. After your sessions, don’t forget to visit the virtual exhibit hall where you can connect with our team across SQL Server 2022, Azure SQL, Azure Synapse Analytics, Microsoft Power BI, Azure Arc, and more.

Register for PASS Data Community Summit today.

The post PASS Data Community Summit keynote: a bridge to a new universe appeared first on Microsoft SQL Server Blog.

The rise of data represents a tremendous opportunity and also poses challenges. Companies are seeing their relational and nonrelational data proliferate exponentially on-premises, in the cloud, at the edge, and in hybrid environments. The most transformative companies drive predictive insights on current data, whereas others may struggle to drive even reactive insights to their historical data. Information may be siloed across geographies and divisions.

To empower customers amid this environment, Microsoft offers an end-to-end data platform of products and services that come together to meet these challenges. Operational databases cover all possible deployment locations, including SQL Server and Azure Arc-enabled data services, Azure SQL fully-managed cloud databases, and Azure SQL Edge for IoT devices. To enable real-time insights, Azure Synapse Analytics brings together data integration, enterprise data warehousing, and big data analytics, and customers can visualize their data with Power BI. Customers can discover, catalog, and govern their data wherever it resides with Azure Purview.

SQL Server 2022 integrates with Azure Synapse Link and Azure Purview to enable customers to drive deeper insights, predictions, and governance from their data at scale. Cloud integration is enhanced with disaster recovery (DR) to Azure SQL Managed Instance, along with no-ETL (extract, transform, and load) connections to cloud analytics, which allow database administrators to manage their data estates with greater flexibility and minimal impact to the end-user. Performance and scalability are automatically enhanced via built-in query intelligence. There is choice and flexibility across languages and platforms, including Linux, Windows, and Kubernetes.

Azure-enabled

Bi-directional HA/DR to Azure SQL

To ensure uptime, SQL Server 2022 is fully integrated with the new link feature in Azure SQL Managed Instance. With the new link feature for Azure SQL Managed Instance, you now get all the benefits of running a PaaS environment applied to disaster recovery—allowing you to spend less time on setup and management even when compared to an IaaS environment. This works by using a built-in Distributed Availability Group (DAG) to replicate data to a previously deployed Azure SQL Managed Instance as a DR replica site. The instance is ready and waiting for whenever you need it—no lengthy configuration or maintenance is required. You can also use this link feature in read scale-out scenarios to offload heavy requests that might otherwise affect database performance. And we are working on building out more capabilities to support bi-directional data movement.

Azure Synapse Link

Previously, moving data from on-premises databases, like SQL Server, to Synapse required you to use ETL. As we all know, setting up and running an ETL pipeline takes a lot of work, and insights lag behind what is happening at any moment. Azure Synapse Link for SQL Server 2022 provides automatic change feeds that capture the changes within SQL Server and feed those into Azure Synapse Analytics. It provides near real-time analysis and hybrid transactional and analytical processing with minimal impact on operational systems. Once the data hits Synapse, you can combine it with many different data sources regardless of their size, scale, or format and run powerful analytics over all of it using your choice of Azure Machine Learning, Spark, or Power BI. Since the automated change feeds only push what is new or different, data transfer happens much faster and now allows for near real-time insights, with minimal impact on the performance of the source database in SQL Server 2022.

Azure Purview integration

We recently announced the general availability of Azure Purview as a unified data governance and management service. We are excited to highlight that SQL Server is also integrated with Azure Purview for greater data discovery, allowing you to break down data silos. Through this integration you will be able to:

• Automatically scan your on-premises SQL Server for free to capture metadata.
• Classify data using built-in and custom classifiers and Microsoft Information Protection sensitivity labels.
• Set up and control specific access rights to SQL Server.

Enhancements to performance, security, and availability

Performance

SQL Server offers differentiated performance, with number one OLTP performance¹ and number one Non-Clustered DW performance on 1TB², 3TB³, 10TB⁴, and 30TB⁵ according to the independent Transaction Processing Performance Council. Built-in query intelligence in SQL Server 2022 innovation includes:

• For Query Store, we are adding support for read replicas and enabling query hints to improve performance and quickly mitigate issues without having to change the source T-SQL.
• For Intelligent Query Processing, we’re expanding more scenarios based on common customer problems. For example, the “parameter sensitive plan” problem refers to a scenario where a single cached plan for a parameterized query is not optimal for all possible incoming parameter values. With SQL Server 2022’s Parameter Sensitive Plan optimization feature, we automatically enable the generation of multiple active cached plans for a single parameterized statement. These cached execution plans will accommodate different data sizes based on the provided runtime parameter values.

Security

Over the past ten years, SQL Server has had fewer vulnerabilities than the competition.⁶ Building on this, the new ledger feature creates an immutable track record of data modifications over time. This protects data from tampering by malicious actors and is beneficial for scenarios such as internal and external audits.

Availability

With the move to a more global distribution of workers and customers, many organizations are moving to a multi-write environment that allows changes to be made to the local database and pushed out to other replicas in a two-way flow of updates. However, if multiple people change the same row in the database and the different write replicas have different information in them, previously this peer-to-peer replica conflict would stall the whole operation until it was addressed. With SQL Server 2022, we are automating the last-writer wins rule. Now, when a conflict is detected, the most recent modification time will be chosen to be persisted on all replicas. This helps keep your multi-write scenarios running smoothly.

Learn more and apply for the preview today

At this point in time, we are onboarding a limited number of customers and partners to our SQL Server 2022 preview, in advance of public preview and general availability in the coming year.

Learn more about the SQL Server 2022 release on our webpage and by viewing deep-dive sessions at Microsoft Ignite, with Microsoft Mechanics, and at the upcoming free virtual PASS Data Community Summit. Read more about Azure at Ignite on the Azure blog.

Register today to apply for the SQL Server 2022 preview and stay informed about SQL Server 2022 updates.

All TPC Claims as of October 6, 2021

¹ http://www.tpc.org/4087

² http://www.tpc.org/3374

³ http://www.tpc.org/3380

⁴ http://www.tpc.org/3362

⁵ http://www.tpc.org/3364

⁶ National Institute of Standards and Technology Comprehensive Vulnerability Database

The post Announcing SQL Server 2022 preview: Azure-enabled with continued performance and security innovation appeared first on Microsoft SQL Server Blog.

Today, we’re proud to announce the release of the latest cumulative update, CU13, for SQL Server Big Data Clusters which includes important changes and capabilities:

• Hadoop Distributed File System (HDFS) distributed copy capabilities through azdata
• Apache Spark 3.1.2
• SQL Server Big Data Clusters runtime for Apache Spark release 2021.1
• Password rotation for Big Data Cluster’s auto-generated Active Directory service accounts during BDC deployment
• Enable Advanced Encryption Standard (AES) Optional parameter on the automatically generated AD accounts

Major improvements in this update are highlighted below, along with resources for you to learn more and get started.

HDFS distributed copy capabilities through azdata

Hadoop HDFS DistCP is a command line tool that enables high-performant distributed data copy between HDFS clusters. On SQL Server Big Data Clusters CU13 we are surfacing the capability of distcp through the new azdata bdc hdfs distcp command to enable inter Big Data Clusters distributed data copy. This enables data migration scenarios between SQL Server Big Data Clusters; supporting both secure and non-secure cluster deployment configurations.

For more information, see:

• HDFS distributed data copy—SQL Server Big Data Clusters
• azdata bdc hdfs reference—SQL Server Big Data Clusters

Apache Spark 3.1.2

Up to cumulative update 12, Big Data Clusters relied on the Apache Spark 2.4 line, which reached its end of life in May 2021. Consistent with our continuous improvement commitment to the Big Data and Machine Learning capabilities of the Apache Spark engine, CU13 brings in the current release of Apache Spark, version 3.1.2.

This new version of Apache Spark brings stellar performance benefits on big data processing workloads. Using the reference TCP-DS 10 TB workload in our tests we were able to reduce runtime from 4.19 hours to 2.96 hours, a 29.36 percent improvement achieved just by switching engines while using the same hardware and configuration profiles, no additional application optimizations. The improvement mean of individual query runtime is 36 percent.

Spark 3 is a major release and as such, contains breaking changes. Following the same established best practice in the SQL Server universe, perform a side-by-side deployment of SQL Server Big Data Clusters to validate your current workload with Spark 3 before upgrading. You can leverage the new azdata HDFS distributed copy capability to have a subset of your data needed to validate this workload. For more information, see the following articles to help you assess your scenario before upgrading to the CU13 release:

• Spark 3 upgrade guide—SQL Server Big Data Clusters
• Migration guide: Spark Core—Spark 3.1.2 documentation

SQL Server Big Data Clusters runtime for Apache Spark release 2021.1

With this release of SQL Server Big Data Clusters, we doubled down on our commitment of release cadence, binary compatibility, and consistency of experiences for data engineers and data scientists through the SQL Server Big Data Clusters runtime for Apache Spark initiative.

The SQL Server Big Data Clusters runtime for Apache Spark is a consistent versioned block of programming language distributions, engine optimizations, core libraries, and packages for Apache Spark.

Here is a summary of the SQL Server Big Data Clusters runtime for Apache Spark release 2021.1 shipped with SQL Server Big Data Clusters CU13:

• Apache Spark 3.1.2
• Scala 2.12 for Scala Spark
• Python 3.8 for PySpark
• Microsoft R Open 3.5.2 for SparkR and sparklyr

For more information on all included packages and how to use it, see:

• SQL Server Big Data Clusters CU13 release notes—SQL Server Big Data Clusters
• Spark library management—SQL Server Big Data Clusters

Password rotation for Big Data Cluster’s Active Directory service accounts

When a big data cluster is deployed with Active Directory integration for security, there are Active Directory (AD) accounts and groups that SQL Server creates during a big data cluster deployment, see auto-generated active directory objects for further information.

When it comes to security-sensitive customers, it is usually required security reinforcement such as setting password expiration policies, allowing the administrator to set user passwords to never expire or expire after a certain number of days. For SQL Server Big Data Cluster deployments it was previously required to manually rotate the password for those auto-generated active directory objects.

With SQL Server Big Data Clusters CU13, we are now releasing the azdata bdc rotate command to rotate passwords for all auto-generated accounts except for the DSA account. In order to update the DSA password for SQL Server Big Data Clusters we are releasing a specific operation notebook.

Enable Advanced Encryption Standard (AES) on the automatically generated AD accounts

Today’s enterprise environments are facing a lot more challenges than it used to be. Using secure and encrypted connections when authenticating with Kerberos will significantly lower the risk to encounter attacks such as Kerberoasting; a type of attack targeting service accounts in Active Directory.  Starting with SQL Server Big Data Clusters CU13, we’re enabling the Advanced Encryption Standard (AES)  support on the auto-generated AD accounts by allowing users to set an optional boolean parameter in the BDC deployment profile to indicate this AD account supports Kerberos AES 128 bit and 256 bit encryptions.

For more information, see:

• SQL Server Big Data Clusters CU13 release notes—SQL Server Big Data Clusters
• Management notebooks for SQL Server Big Data Clusters—SQL Server Big Data Clusters

Ready to learn more?

Check out the SQL Server Big Data Clusters CU13 release notes to learn more about all the improvements available with the latest update. For a technical deep-dive on Big Data Clusters, read the documentation and visit our GitHub repository.

Follow the instructions on our documentation page to get started and deploy Big Data Clusters.

The post What’s new with SQL Server Big Data Clusters—CU13 Release appeared first on Microsoft SQL Server Blog.

Administrators can enable platform logging and metrics to one of their Azure services such as Azure SQL and set the destination to Log Analytics workspace. By installing native Azure Monitor Logs extension in Azure Data Studio, users can connect, browse, and query against Log Analytics workspace. Data professionals who are using Azure SQL, Azure PostgreSQL, or Azure Data Explorer in Azure Data Studio can access the data in the Log Analytics workspace for diagnosis or auditing in that same development environment. This native Azure Monitor Logs extension allows Azure service customers to also author notebooks with Log Analytics kernel, all equipped with Intellisense.

Examples for Azure SQL users

While the examples in this section are specific to Azure SQL scenarios, Azure Monitor Logs can also capture events from other Azure resources, such as Azure Functions or Azure Web Apps. Now, let’s dive into these examples where users can leverage Log Analytics workspace data to better understand an Azure SQL database.

Example One: Who dropped a table in my database?

Connect to your Log Analytics workspace that captures the Azure SQL audit events. Right-click and choose New Query from the menu. Copy the following query and paste it to Query editor in Azure Data Studio.

AzureDiagnostics
| where action_name_s == "BATCH COMPLETED"
| where statement_s contains "DROP TABLE"
| project TimeGenerated, Category, OperationName, server_principal_name_s, statement_s
| sort by TimeGenerated desc 
| take 10

In this example, a good question to ask might be, why are these users executing a lot of drops during this time?

Example Two: What type of errors happen?

Connect to your Log Analytics workspace that captures the Azure SQL error events. Right-click and choose New Notebook from the menu. Copy the following query and paste it to Query editor in Azure Data Studio.

AzureDiagnostics
| where OperationName == "ErrorEvent"
| extend ErrorNumber =  toint(error_number_d) 
| summarize event_count=count() by EventTime = bin(TimeGenerated, 2d),  ErrorNumber
| evaluate pivot(ErrorNumber, sum(event_count))
| sort by EventTime asc

In this example, we see a timeline with two-day intervals that maps error count by error number. A good question would be to understand why error 208 happens a lot.

Overall benefits

Here are four key benefits of using Azure Monitor Logs extension in Azure Data Studio.

1. Efficiency in data exploration and data analysis. Users now have access to their SQL data sources in the same place as their Log Analytics workspaces. Users can also use Sand Dance extension to further enhance their data exploration and analysis.
2. Reproducible analysis and diagnosis with notebooks.
3. Improved DevOps troubleshooting experience with Azure Monitor Logs notebooks. This is illustrated in the previous examples.
4. Version control the queries and notebooks directly with git in Azure Data Studio. Users can also add these files as part of their CI/CD pipelines in GitHub or Azure DevOps.

Other related extensions: Kusto (KQL) extension which works for Azure Data Explorer

How to get started

• Azure Monitor Logs Extension for Azure Data Studio (preview)
• Create diagnostic settings to send platform logs and metrics to different destinations
• SQL to Kusto cheat sheet
• What is Azure Monitor Logs?
• Using SandDance visualizations

This preview release is the beginning of a strategic journey to richer end-to-end DevOps with Data in Azure Data Studio. Please feel free to submit your suggestions and bugs on GitHub.

The post Enhance your Log Analytics data exploration in Azure Data Studio appeared first on Microsoft SQL Server Blog.

Each single database in Azure SQL Database service is guaranteed its own dedicated compute, memory, and storage resources that you can scale dynamically. If you need to scale out the storage and compute resources substantially beyond the limits for the general purpose and business critical service tiers, you’re in luck. The hyperscale service tier provides highly scalable storage and compute performance that leverages the Azure architecture to meet the largest needs of an Azure SQL Database.

The benefits of hyperscale

With the hyperscale service tier, Microsoft removed many of the limits seen in other cloud databases. For example, most other databases are limited by the resources available in a single node. You don’t have those limits in the hyperscale service tier. Hyperscale databases aren’t created with a defined maximum size. A flexible storage architecture allows a hyperscale database to grow as needed. And you’re only billed for the capacity you use.

The hyperscale service tier also won’t box you into your initial configuration. Hyperscale databases can be backed up almost instantaneously—regardless of the volume of data in the database. You can also scale a database in the tens of terabytes in minutes.

Do you need to go hyperscale?

The hyperscale service tier supports a broad range of SQL Server workloads, from pure online transactional processing (OLTP) to pure analytics. However, it’s optimized for OLTP and hybrid transaction and analytical processing (HTAP) workloads. That said, hyperscale provides flexibility and high performance, two features that can benefit most business workloads. However, hyperscale will be particularly advantageous to you in the following scenarios:

• If you have a large on-premises database and want to modernize applications by moving them to the cloud.
• If you’re running databases in Azure but are limited by the database size restrictions of the other service tiers.
• If you have smaller databases that require fast compute scaling, high performance, instant backup, and fast database restore.

The hyperscale architectural model provides nearly instantaneous database backups with no impact on compute resources, the database restores in minutes rather than hours or days, and there is higher overall performance, and rapid scale-out and scale-up. Now there’s no need to worry about how much or how fast your database will grow. The hyperscale service tier in Azure SQL Database has you covered.

To learn more about moving SQL Server to the cloud, download Future-Proof Your Data Infrastructure with Azure: A Business Case for Database Administrators.

The post Use hyperscale for your most demanding cloud-born workloads appeared first on Microsoft SQL Server Blog.

The truth is, there’s no need to leave all of your skills behind as you embark on your new Azure adventure. You just need to enhance the skills you already have with new and exciting ones. Much of our value around automation, understanding mission critical systems and development, and optimization is just as needed in the new world of the cloud as it was in the world of on-premises data centers. Those Powershell and other scripting skills can make you an asset when it comes to performing tasks and deploying fast into the cloud.

To help you on your way to mastering the cloud, we’re offering a free eBook from Packt, Professional Azure SQL Administration, second edition, which will get you up to speed on the Azure cloud quicker than you ever could on your own. This eBook is chock full of all of the technical knowledge you’ll need, no matter if you are a novice or have been working in Azure extensively and just want a reference containing valuable tips and best practices.

One of the focuses I really appreciate in this edition is the fine detail on Azure security, which is a common hot topic for the business when it’s considering the cloud. The book also goes over all of those cool new features that are available first in the cloud, so you won’t have to wait to learn about data synchronization between Azure databases, machine learning in Azure Database, or sharing data from the sidelines.

There are over 500 pages in this eBook that will educate you on the service and pricing tiers in Azure, offer guidance on how to build out a successful migration project, and once you’ve migrated to Azure, how to get the most out of your cloud investment.

The post DBAs: It’s time to get up to speed with Azure appeared first on Microsoft SQL Server Blog.

To limit the amount of changes required for the above scenarios, UTF-8 is enabled in existing the data types CHAR and VARCHAR. String data is automatically encoded to UTF-8 when creating or changing an object’s collation to a collation with the “UTF8” suffix, for example from LATIN1_GENERAL_100_CI_AS_SC to LATIN1_GENERAL_100_CI_AS_SC_UTF8. Refer to Set or Change the Database Collation and  Set or Change the Column Collation for more details on how to perform those changes. NCHAR and NVARCHAR remain unchanged and only allow UTF-16 encoding.

UTF-8 is only available to Windows collations that support supplementary characters, as introduced in SQL Server 2012. You can see all available UTF-8 collations by executing the bellow command in your SQL Server 2019 CTP:

SELECT Name, Description FROM fn_helpcollations() 
WHERE Name like '%UTF8';

Additionally, if your dataset uses primarily Latin characters, significant storage savings may also be achieved as compared to UTF-16 data types. For example, changing an existing column data type from NCHAR(10) to CHAR(10) using an UTF-8 enabled collation, translates into nearly 50 percent reduction in storage requirements. This is because NCHAR(10) requires 22 bytes for storage, whereas CHAR(10) requires 12 bytes for the same Unicode string.

Getting started

• To get started with SQL Server 2019 preview, find download instructions on the SQL Server 2019 web page.
• You can read more about Unicode support in SQL Server, including details on UTF-8 support.

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