Margarita Naumova
CEO, Data Platform Architect at Inspirit
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Magi Naumova is an SQL Server Architect and Consultant, speaker and trainer, Microsoft Certified Master, MVP, MCT, Founder of SQL Master Academy, and founder and CEO Of Inspirit Data Platform Consulting Company in Bulgaria and Norway, founder, and the leader of Bulgarian SQL & BI User group and Azure Analytics User Group BG. She has more than 25 years SQL Server training and consulting experience. Magi is a former member of Microsoft Services Worldwide Technical Leadership Team. Currently she speaks, writes, trains and consults people on SQL Server and MS Fabric Data Platform. She is MVP for 14 years in a row. Her unique SQL Master Academy training program has helped hundreds of specialists to feel knowledgeable in their daily work or to find an inspirational career path in the world of SQL Server.
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Area of Expertise
Performance tuning with Copilot. Are we there yet?
Performance tuning in Azure is critical and at the same time not an easy task. It is important we put effort and time in it as proper optimization will reduce the resource consumption and cost. With the release of Copilot for Azure SQL, we got a new and promising way to make this effort easier and to get the answers to our questions fast. It sounds tempting to be able to get some copilot-ed help for questions like where the major cost spending is, why and how to reduce it, what are the active connections running right now or what are the top high CPU queries run in the last week? Even if it’s tempting, we still have some doubts using it, is it useful or it is just a funny toy to play with? Shall we trust it or its just yet another fancy tool out there? Come to this session and let's experience together performance tuning with Copilot and let's see what we can rely on and what to expect.
Next-Level Data: Exploring SQL Server 2025 Innovations
SQL Server 2025 is more than just an update — it's a leap forward in how we store, process, and extract value from data. In this session, we’ll dive into the most impactful new features and enhancements that make this release a game changer for data professionals. From native vector data types and AI-powered search to performance optimizations, enhanced security, and cloud-connected capabilities, you'll get a guided tour of what's new, why it matters, and how to take full advantage of it in your environment. Whether you're a DBA, developer, or architect, this session will equip you with practical insights and real-world examples to help you level up your SQL Server skills for 2025 and beyond.
Next generation Data Warehousing with Microsoft Fabric
Microsoft Fabric provides customers with a unified product that addresses every aspect of their data estate by offering a complete, SaaS-ified Data, Analytics and AI platform, which is lake centric and open. What does it mean for Data warehousing and how it changes the game in the BI and Data Engineering space? Come to this session to share the exciting new feature of Cloud Analytics with me. I will talk about Lakehouse, Datawarehouse, Data Mesh and much more on what I think will be part of our job next few years.
Next generation Data Warehousing with Microsoft Fabric
MS Fabric has opened a whole new world of data warehousing! The change is not just about the technology; it’s a paradigm shift in the data warehouse field. Come to this session to share the exciting new feature of Cloud Data warehousing. Let’s see how the next generation data warehouse looks like and what will change with the release of MS Fabric. We will discover the Fabric Datawarehouse, Lakehouse, and how to cross them with shortcuts and queries. You will see how democratizing and SaaS-ifying the Datawarehouse opens a whole new set of scenarios, and how to leverage a Lakehouse together with a Warehouse for a best of breed analytics strategy!
MS Fabric & Databricks - Happily ever after or Clash of the Titans
What is happening in the field of Data Engineering and what should we expect in the future? Which platform for data engineering to focus on if you are Data Engineer?
Databricks is a cloud-based data processing platform that provides a collaborative environment for data scientists, engineers, and analysts. On the other hand, Fabric is a unified analytics platform that brings together all the data and analytics tools that organizations need. Will this be a battle or a good working union? Let’s explore the key differences between Microsoft Fabric and Databricks from many angles including technical specifics and capabilities, experience from projects so far and even pricing. Let’s also talk about use cases of both platforms.
Lakehouse vs Data Warehouse vs KQL Database: Use Cases and Architecture Designs
What architecture designs and solutions are best for analytics in Fabric, what to choose for building your solution – Lakehouse, Data Warehouse or KQL Database and where to start? You will find all the answers during this training day! We will dive into all 3 items - Lakehouse, Data Warehouse and KQL Database to learn their specifics and their best usage. We will emphasize the main differences and talk about typical uses cases. Then we will pay a special attention to common Architecture Designs for using Data Warehouse, Data Lakehouse, and Real-Time Analytics/KQL Database. At the end you will discover how your data estate for analytics data warehousing/reporting will change or differ from existing designs and how to choose the right path moving forward. This training day will give you a very good understanding of the differences between the Data Warehouse, Data Lakehouse, and KQL Database, and most important it will explore for you possible Fabric solution designs and use cases to get the best of the Data Warehouse, Data Lakehouse, and Real-Time Analytics/KQL Database. Isn’t it that you need to know to start or continue your journey to a Modern Analytics Platform in Fabric as data engineer or data architect?
Lakehouse vs Data Warehouse vs KQL Database: Use Cases and Architecture Designs
What architecture designs and solutions are best for analytics in Fabric, what to choose for building your solution – Lakehouse, Data Warehouse or KQL Database or a combination of them? How and where to start? This session will give you the answers and even more. We will dive into all 3 experiences - Lakehouse, Data Warehouse and KQL Database and discover their best usage. We will emphasize the main differences and talk about typical uses cases. Then we will pay a special attention to common Architecture Designs for using Data Warehouse, Data Lakehouse, and Real-Time Analytics/KQL Database. At the end you will discover how to choose the right path moving forward. Isn’t it that you need to know to start or continue your journey to a Modern Analytics Platform in Fabric as data engineer or data architect?
Keeping historical data in tables forever – mission (im)possible!
Data growth can easily become a problem soon after deploying a database in production, especially if data layout and data management was not planned. Let’s discover some solutions for keeping historical data in the database when you receive near to impossible requirements like storing data in same tables forever and being able to edit and query them at the same time, of course keeping the response at its best. Will combining partitioning, temporals, CSI, and other indexing with different loading/unloading/deletion make it possible? The session starts by defining the frame requirements coming from real projects and goes through different alternatives and possible solutions, their benefits, and drawbacks. Based on a real project case the session walks you through the design process from the start to the reaching of the final solution and making the client (and developers) happy.
GenAI in SQL Server 2025: What Works, What Doesn’t, and Why It Matters
SQL Server 2025 introduces native support for vectors, AI model integration, and RAG workloads—but what does this mean for system design, performance, and scalability? This session takes a critical look at the architectural implications of using SQL Server for GenAI applications. We’ll explore how vector indexes work, how embedding data affects performance, and when SQL Server is (or isn’t) the right tool for the job. Ideal for architects and DBAs looking to make informed decisions in AI-powered data platforms.
This session is for practitioners who want to go beyond the buzzwords and understand what it takes to use SQL Server effectively in AI-powered applications—what works, what’s still experimental, and what to watch out for.
From Load to Logic: Designing for Performance in Microsoft Fabric Warehouses
Microsoft Fabric Data Warehouse is not just another cloud database — it’s the T-SQL analytics backbone in Azure and the strategic destination for modernizing on-premises SQL-based data warehouses. This session will dive deep into designing for performance and scalability in Fabric, equipping you with the patterns, techniques, and real-world practices to get the most out of this SaaS-based solution. This session is your guide to mastering the SQL path in Fabric: where familiar skills meet enhanced cloud-native performance.
Whether you're migrating from an on-premises SQL Server or Synapse Dedicated SQL Pools, understanding how to organize your data loads, structure your models, and tune your workloads is critical. We'll explore enhanced query performance features such as result set reuse, automatic statistics, query plan caching, and intelligent workload management — all designed to give you cloud-grade agility without giving up the control you expect from traditional systems.
Learn how to navigate limitations, optimize for throughput and concurrency, and make the most of Fabric's underlying resource management. This session is your guide to aligning enterprise-grade SQL data warehousing with the simplicity and scale of the cloud.
Designed for professionals who live and breathe SQL, this session shows how to bring enterprise-grade data warehousing into the Fabric era — without sacrificing control, performance, or architectural clarity.
A Table Partitioning Deep Dive - Part 2
From zero to hero, understand table partitioning now and forever! Partitioning is not a silver bullet of our biggest tables! Many thinks can go wrong. Before we consider it, we need to understand how it works. In this demo-rich session we will cover very much of what do you need to know about it. In the Part 1 of this 100 min session we will start from table partitioning mechanics and will dive very fast into mastering it. We will cover partition elimination, aligned and non-aligned indexes. In the Part 2 we will pay special attention on choosing the partition key, will touch a bit reading query plans of partitioned tables, and troubleshooting performance problems. At the end of this deep dive session, you will get a very good understanding of how the partitioning works and how it helps for better data management and maintenance. On top of technical demos, I will give you examples from many real cases I’ve gone through. You will get ideas on how to approach more complex cases of historical data maintenance, by combining partitioning, indexing, and records deletion. You will know how to achieve performance improvement, and when you should not choose partitioning but some other optimization techniques instead.
Ask the Expert - Group A (Wed 11:10 to 13:00)
'Got a problem? Don't know who to call? Bring it to SQLBits and get it solved by our panel of experts!
More information can be found here:
https://sqlbits.com/news/ask-the-experts-at-the-experts-lounge/
1. SQL and Databases:
2. Microsoft Fabric and Related Technologies:
3. Power BI and Data Visualization:
4. Azure and Cloud Technologies:
11:10 to 12:00
1. Magi Naumova
2. Paul Andrew, Brynn Borton
3. James Dales, Ana-Maria Bisbe York
4.
12:10 to 13:00
1. Martin Catherall & Heid Hasting
2. Freddie Santos, Nagaraj Sengodan
3. Kristoffer West, Mark Hayes
4.
Approaching the biggest tables in your database – strategies and best practices
Ever-growing multimillion rows tables are challenging to work with. It’s not about the storage, it’s about keeping the performance of your workload even when data grows forever. What options do we have? Should we use partitioning, deletion and archiving, or some indexing and temporals? When to use what and how? Let’s discover some strategies for data maintenance before it becomes even more difficult to implement them.
A Data Lakehouse walkthrough with Synapse Analytics
Discover the Lakehouse concept as a potential roadmap of your Modern DW in the cloud.
During this session we will go through creating different types of objects in Synapse Serverless, combining them in a database-like solution, creating delta format in Spark and using it in a Lake database in Serverless. If you speak T-SQL and understand ADL, then this session will help you to level up fast in the data engineering track by understanding Synapse Serverless and the concept of Data Lakehouse in practical examples. You will learn some tips and trick on the way, and I promise I will not speak Phyton (too) much!
Data Lakehouse with Azure Synapse Analytics
Synapse Analytics combines the use of relational datawarehouse and a big data ADL in one single workspace. Data lakehouse on the other hand is the ability to query data directly and in the same way no matter if the data are in data lake or relational datawarehouse. Is it just the workspace that makes the concept of Data lakehouse real in Synapse Analytics? Let discover what we can do with Synapse serverless, how to architect data analytics in the future and do these new trends come to say that the traditional DW is dead?
Azure SQL Managed Instance - what, how, when (and how much)
Do you want to learn more about SQL MI? Then this session is a good start. I will show you how it looks like, how to migrate your databases to MI and of course I will talk about the price. I believe some common use case scenarios will pop up and you will be able to plan your path to SQL MI.
Modern database design (anti)patterns
The database tier is quite often neglected in the application design process, which results in performance issues, lack of scalability and even disruption of service. Design choices that seem great at the beginning fail totally on scale and performance when the application goes in production. We must realize that patterns which were valid 10 years ago are less likely to work now, like cursor logic, xml usage, or storing all in db v/s using NoSQL. From global industry trends to specific database patterns, this session is a combination of best practices, good and bad patterns, tips, and tricks which I give to customers in my work as a consultant. Good design choices from the beginning help avoiding complex and expensive redesign in the future!
feedback link: https://sqlb.it/?7315
SQLBits 2022 Sessionize Event
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