Intelligent Query Processing in Action: A Demo-Heavy Guide to Smarter Query Performance

Can we enhance query performance without any code changes? Modifying applications can be an expensive endeavor or completely beyond your control. Therefore, developers and DBAs prefer that the query processor adapts to their workload requirements rather than relying on options and trace flags to improve performance. Adaptation is the foundational concept behind Intelligent Query Processing (IQP) in the latest versions of SQL Server.

In this fast-paced, demo-focused session, we’ll take a practical tour of IQP features introduced in SQL Server 2022, 2019, and 2017. You’ll see how each feature solves specific performance issues. We’ll also briefly cover how to enable these features at various scopes—server, database, session, or query—and identify those that rely on Query Store.

Attending this session will allow you to learn about the new capabilities of intelligent query processing and gain powerful tools to persuade your peers to upgrade SQL Server and databases to the latest build, both on-premises and in the cloud.

PostgreSQL vs. SQL Server: Backup and Restore Strategy Differences

Reliable backup and restore strategies are critical for ensuring high availability and disaster recovery in database environments. For SQL Server experts exploring PostgreSQL, understanding the fundamental differences in how these systems handle backups is essential. This session will compare the backup and restore mechanisms of SQL Server and PostgreSQL, focusing on full, differential, and transaction log backups, as well as point-in-time recovery workflows. Aimed at database administrators and developers, the presentation will highlight how each system approaches backup granularity, recovery models, and tooling to support resilient operations.

For example, did you know that:

-SQL Server supports per-database full, differential, and transaction log backups, while PostgreSQL’s native physical backups operate at the cluster level.
-SQL Server’s differential backups track changes since the last full backup, whereas PostgreSQL relies on continuous Write-Ahead Log (WAL) archiving for incremental recovery.
-SQL Server provides integrated backup and restore commands, while PostgreSQL depends on external utilities.

SQL Database in Microsoft Fabric: Redefining Modern Data Workflows

With the introduction of Microsoft Fabric, a new flavor of SQL Database has emerged—built to power unified data experiences across analytics, AI, and business intelligence. But what sets it apart from traditional SQL Server or Azure SQL Database?

In this session, we’ll unpack the rationale behind this new SQL engine and explore its advantages for data professionals. You’ll learn how SQL in Fabric simplifies data integration, enhances collaboration through native support for Notebooks and Power BI, and extends analytics possibilities with built-in support for Azure OpenAI and vector-based queries.

Expect a practical walkthrough: from initial setup and data ingestion to generating insights with Power BI and monitoring performance. We'll also explore how Fabric’s SQL engine fits into the broader data architecture—and whether DBAs and data engineers need to “retrain” or simply “retool.”

PostgreSQL vs. SQL Server: Security Model Differences

Security is paramount in database management. If you are an SQL Server expert looking to learn PostgreSQL, it is essential to understand how PostgreSQL's security model differs from that of SQL Server. This talk will compare the security models of both database systems. Aimed at database administrators and developers, the presentation will highlight the key differences in how these systems handle user authentication, roles, and permissions.

For example, did you know that:
-SQL Server distinguishes between logins and users, whereas PostgreSQL uses a unified role-based system for authentication and authorization.
-SQL Server offers predefined server and database roles, such as sysadmin, which provides a range of out-of-the-box permissions. Conversely, PostgreSQL includes default roles like pg_read_all_data, designed to simplify standard permission sets.
-SQL Server allows the creation of custom roles with flexible permission assignments. PostgreSQL's roles enable inheriting permissions from other roles and support complex role hierarchies.

Understanding these differences and others discussed during the session will enhance your grasp of the security model distinctions between SQL Server and PostgreSQL, enabling you to implement security best practices effectively in either environment.

Session Goals:
1. Develop a strong understanding of PostgreSQL's role-based authentication and authorization system.
2. Compare SQL Server's security structure with PostgreSQL's to improve database security management and to ensure secure migrations.

Session Prerequisites:
1. Basic Understanding of SQL Server Security Concepts.
2. Fundamental PostgreSQL Knowledge (Optional but Helpful).
3. General Database Administration & Security Awareness.

Mastering Kusto Query Language (KQL): Your Gateway to Azure Monitoring and Insights

As organizations move workloads to Azure, visibility into resource performance, security, and operations becomes essential. Azure Monitor is Microsoft’s centralized platform for observability, bringing together telemetry across applications, infrastructure, and services. At the core of this platform is Kusto Query Language (KQL)—a fast, expressive language purpose-built for exploring log data and enabling deep analytics.

This session is your hands-on introduction to KQL, designed for cloud professionals who want to gain actionable insights from Azure Monitor Logs. Through live demos and practical examples, we’ll walk through the core concepts and real-world applications of KQL, including:

Understanding KQL syntax and how it compares to T-SQL

Querying and analyzing log data using Azure Log Analytics

Efficient techniques for filtering, sorting, and summarizing data

Building alerts and identifying anomalies with KQL-based queries

Using joins, time-series functions, and advanced features for deeper analysis

By the end of the session, you'll be equipped to confidently write and use KQL queries to enhance monitoring, security, and operational decision-making in Azure environments.

🎓 Prerequisites:

Basic understanding of SQL or T-SQL

General familiarity with Azure services

The DBA’s Survival Guide: 360° SQL Server Backup and Restore

If you are the database steward, your most critical task is to guarantee that all committed transactions are always recoverable during a disaster within acceptable limits for data loss and downtimes.

Achieving this can be simple, such as taking a full backup, or complex, which might include filegroup backups based on the size and criticality of your application data.

Whatever your situation is, being well-prepared and practicing with your tools, scripts, and strategy will ensure you can respond quickly and efficiently when a disaster happens.

In this session, I will teach you all the basic types of backups and how to create backups and restores using SSMS and TSQL. Then we will move to advanced techniques, discussing file and filegroup backups, partial database restore, and T-SQL snapshot backups introduced with SQL Server 2022.

At the end of the session, you can create a solid Backup and restore strategy based on the service level agreement you and your business counterpart have agreed to.

Prerequisites:

Basic understanding of SQL Server architecture
Familiarity with SSMS and T-SQL

Learning Objectives:

Understand and implement various SQL Server backup types—from full and differential to filegroup and snapshot backups.

Design a disaster recovery strategy tailored to your business SLAs, ensuring minimal downtime and data loss.

Leveraging Azure AI and Python for Data-Driven Decision Making

In this technical talk, we will explore how to harness the power of Azure AI, Azure AI Studio, Azure Search Services, and large language models to extract valuable decision-making data from the Azure SQL Database.

We will begin by discussing Azure AI and its capabilities. Starting with a clean slate, build a solution using Azure AI Studio and its user-friendly interface that can chat with an SQL database, helping make data-driven decisions without writing code. This solution will delve into Azure Search Services, highlighting how it can be used to efficiently index and query data.

The second part of the presentation will focus on utilizing large language models and Python notebooks to extract and analyze data from the Azure SQL Database. Attendees will learn how to set up their environment, connect to the database, and implement AI-driven solutions (talk to the database).

By the end of the session, participants will have a solid foundation in using Azure AI and Python for data-driven decision-making, empowering them to leverage these tools in their projects.

(Lightning talk) Break five common myths about SQL Server backup

In this session, I will discuss five common myths about SQL Server backup and demo each of them to show you the correct answer.

1. Does Full and Differential backup break the log chain?
2. Are Differential backups incremental?
3. What backups are allowed on system databases?
4 Is transactional backup necessary during full backup?
5. Does backup use a buffer pool to read data pages?

Think like the Cardinality Estimator

SQL Server uses a phase during query optimization, called cardinality estimation (CE). This process makes estimates based on the statistics as to how many rows flow from one query plan iterator to the next. Knowing how CE generates these numbers will enable you to write better TSQL code and in turn, influence the type of physical operations during query execution. Based on that estimated rows, the query processor decides how to access an object, which physical join to use, and how to sort the data. Do you know how the CE generates these numbers? If your query has only one predicate, the query optimizer will use the histogram to estimate how many rows will be qualified. What happens when you have multiple predicates, range predicates, variable values that are “NOT KNOWN” to the optimizer, or you have predicate values increasing in ascending order? Do you know what will happen if your predicate is using an amount that is outside of the histogram range?

In this session, I will show you how the cardinality estimator estimates in all of these scenarios. You will walk out of this session with a clear understanding of how the CE generates its numbers and is ready to tackle those nasty, hard-to-solve query plans.

What the heck is a checkpoint, and why should I care?

In SQL Server, a checkpoint is an internal process that writes dirty pages and transaction log records from memory to disk and marks a point in the transaction log. An 8K page is the fundamental data storage unit in SQL Server.

SQL Server performs every data modification operation in memory (buffer pool) for performance reasons and does not immediately write it back to disk.

This is where checkpoints come into play. There are four types of checkpoints, automatic, indirect, manual, and internal. The Database Engine periodically issues a checkpoint on each database based on the current setting to help reduce the recovery time of a given database from unexpected shutdown to system failure.

This session will explain why you should care and know about the checkpoint process and the different checkpoints that SQL Server does. I will show you exactly what happens during a checkpoint, how you can influence the interval of checkpoints, and changes made with checkpoint settings in SQL 2014 and SQL 2016+.

The magnificent seven and beyond- Intelligent Query Processing in SQL Server

Can we enhance query performance without any code changes? Modifying applications can be an expensive endeavor or completely beyond your control. Therefore, developers and DBAs prefer that the query processor adapts to their workload requirements rather than relying on options and trace flags to improve performance. Adaptation is the foundational concept behind Intelligent Query Processing (IQP) in the latest versions of SQL Server. This demo-intensive presentation will explore the fifteen intelligent query processing features introduced in SQL Server 2022, 2019, and 2017. For each of these fifteen features, we will examine the issue it aims to resolve and the algorithm it uses to tackle the problem. We will evaluate the pros and cons of using these features. You will learn how to deploy them at various scopes tailored to your specific needs, such as server, database, session, or query levels. You will also be able to identify the features built on the Query Store.

Attending this session will allow you to learn about the new capabilities of intelligent query processing and gain powerful tools to persuade your peers to upgrade SQL Server and databases to the latest build, both on-premises and in the cloud.

Prerequisites:
A basic understanding of query processing, familiarity with estimated and actual execution plans, and knowledge of how to measure query execution performance are required.

Goals:
1. Explore the Intelligent Query Processing features introduced since SQL Server 2017.
2. Understand the issues each feature addresses and how it resolves them.
3. Learn how to turn these features on or off at the server, database, session, and query levels.

Azure Database for PostgreSQL: 15 Essential Standards for Compliance and Security

Our team recently inherited multiple Azure Databases for PostgreSQL and discovered the lack of uniform implementation for essential items such as Private Endpoints, Authentication Methods, Backup Retention, Diagnostics Settings, Compute Type, and High Availability.

Uniform standard implementation at the workplace for your PostgreSQL database (even when hosted in Azure) ensures compliance with regulations like GDPR, SOX, PCI DSS, and HIPAA. These regulations ensure institutions maintain robust security measures and audit trails to protect sensitive data and comply with legal requirements, especially in industries like finance and healthcare. Failure to comply with industry regulations can lead to monetary fines, civil penalties, operational restrictions, and, most importantly, reputation damage.

In this session, you will receive a practical checklist of fifteen standards, their importance explained, and actionable insights about implementing them across your company.

Prerequisite:
Familiarity with the features available with Azure Database for PostgreSQL.

Goals:
1. Standardize PostgreSQL Database Implementations in Azure.
2. Improve Compliance with Industry Regulations.
3. Enhance Security and Operational Efficiency.

Lifting Your Data Skills to the Cloud

Ninety percent of enterprises utilize cloud services, 67% of enterprise infrastructure is now cloud-based, and 86% of businesses employ a multi-cloud strategy. What does that mean for data professionals who have worked with on-premises technology for years? Do we have to relearn everything from ground zero? How can we leverage the knowledge and experience we have acquired over the years and apply it to the cloud?
If you are thinking about these questions, this session is for you. Many responsibilities have become shared between database administrators and the cloud provider. For example, in the PaaS model, the cloud provider will take a database backup, while the database administrators are responsible for setting up long-term retention.
We will discuss these critical areas of database administration (System provisioning, Data Migration, Data Security, Performance tuning and monitoring, Disaster Recovery, High Availability, Backup and Recovery, and Cost management). I will show/explain what existing skills we will need and what new ones you will need to learn. I will also cover tools provided by cloud providers that you will need to learn and use.
This session will equip attendees with the necessary information to successfully administer databases in the cloud.

An attendee will walk away knowing three things:
1. Translating On-Premises Expertise to the Cloud.
2. For each critical area of database administration, a list of new skills is required to be a successful "Cloud DBA"?
3. What tools can Cloud DBAs leverage that the provider provides (in this case, Microsoft Azure)?

SQL Server Detective: The DBA's Forensic Toolkit

Your role as a DBA is crucial, as a pivotal figure in the realm of Microsoft SQL Server. Your ability to monitor and analyze your server’s performance and troubleshoot issues is essential, and it's what keeps the system running smoothly. One of the most effective ways to do this is by utilizing the built-in data collection, which records a wealth of information.
This session will delve into six collections (Logs and traces) that can provide valuable insights into your server’s configuration, health, and performance. By the end of this session, you will be equipped with practical knowledge that will empower you to handle any SQL Server issue confidently.

• Default trace
• SQL Error log
• SQL Agent Error log
• System_health
• AlwaysOn_health
• Telemetry xEvents

I will show you each one's default location, retention, and content captured. We will discuss how to access and interpret these logs and demonstrate how they can be used to identify and diagnose issues such as failed logins and deadlocks, the health of the Windows cluster and availability group, and schema changes. By effectively utilizing the SQL Blackbox, you can ensure the smooth operation of your server and maintain the highest performance and reliability.

Prerequisite: Familiar with SQL Server administration

Goals:
1. Know the default location, retention, and content of the six built-in SQL Server logs/collections.
2. Know how information from these collections can be extracted for different troubleshooting scenarios and ensure reliable and highly available database systems.

SQL Server 2025: The Future of Data Starts Here

SQL Server 2025 isn’t just an upgrade—it’s a leap into the future. With powerful AI-ready capabilities and a suite of modern development tools, this release introduces innovations that enhance performance, scalability, and productivity.
In this session, you’ll see demos and real-world examples that highlight key enhancements, such as:
- Advanced JSON processing for greater flexibility
- T‑SQL REST API integration for seamless connectivity
- Expanded T‑SQL features, including fuzzy string matching and regular expression support
- Time-bound Extended Events for precise diagnostics
- Change event streaming for real-time data integration
- Database mirroring in Microsoft Fabric
- Enhanced change tracking and optimized columnstore indexes

Whether you’re a developer, DBA, or architect, this session will demonstrate how SQL Server 2025 can elevate your data platform strategy. Join us to discover what’s new and improved!
Even if you’re fully cloud-based, this session is for you—every feature we cover is available in Azure SQL Database and Azure SQL Managed Instance.