Dennes Torres's Speaker Profile @ Sessionize

Advanced Vector Capabilities in SQL Server 2025: Smarter, Faster, Leaner AI Pipelines

SQL Server 2025 takes vectors from proof-of-concept into production with new features designed for real-world AI workloads. In this advanced session, we’ll explore how the new vector index (DiskANN) enables millisecond-level semantic searches over millions of embeddings, making AI-powered search practical inside SQL Server itself. We’ll look at half-precision vector support, which cuts storage requirements and speeds up similarity calculations — a critical improvement when dealing with large-scale embeddings. And we’ll dive into AI_Generate_Chunks, a function that automates splitting content into semantically meaningful passages, a building block for building retrieval-augmented generation (RAG) pipelines directly within the database.

You’ll learn not just how these features work, but why they matter: how to reduce infrastructure costs, accelerate semantic queries, and simplify the pipeline between your SQL data and your AI models. By the end of the session, you’ll see how SQL Server 2025 transforms vector support from “possible” to production-ready, enabling developers and data professionals to deliver faster, more scalable, and more secure AI-driven applications.

This is not an introduction to vectors — it’s a forward-looking session for attendees who already know the basics and want to see how SQL Server 2025’s latest innovations can elevate their AI architectures.

Allowing nulls in unique fields

There is an interesting workaround to solve this unique problem, let's discover it

AI at Your Fingertips: Building Smarter Apps with Azure Function AI Bindings

Azure Functions are already known for their simplicity—letting you build event-driven solutions faster with triggers, input, and output bindings. Now, with the introduction of AI bindings, you can bring the same developer productivity to AI-powered workloads.

In this session, we’ll explore how the new AI bindings transform the way you integrate AI into your applications:

TextCompletionInput – Start your function with a completion model’s response instead of boilerplate API calls.

EmbeddingsInput – Generate embedding vectors on the fly for search, clustering, and recommendations.

SemanticSearchInput – Build Retrieval Augmented Generation (RAG) scenarios with just one binding.

You’ll see live demos of how these bindings cut down code, simplify integration, and make AI feel like a natural extension of serverless development. By the end, you’ll be ready to build intelligent, cloud-native applications faster than ever.

Azure Open AI and RAG: From Accelerator to Prompt Flow

Open AI and RAG solutions in Azure environment can be very powerful and have many different implementation methods.

On this full day workshop we will start from basic explanation about RAG architectures using OpenAI, passing through the implementation of the ChatWithYourData Solution Accelerator and concluding with Azure ML Prompt Flow to make orchestrations between RAG solutions and other AI solutions

Azure Foundry: Evolving from Models to Agents

The AI landscape is evolving faster than most of use can follow. One year ago we were talking about Models and RAG Architecture and today we talk about Agents, MCP and A2A

In this session we will help to fill the gap between these technologies. We will see how to start from a model and what's needed to transform it into an agent or go even further using PAAS with Azure Foundry

Azure Open AI and RAG: Orchestrating AI Models with Prompt flow

The next step after understanding how to build an LLM architecture using one model is to understand why and how to orchestrate multiple models to reach our purposes.

We will use two different models with different purposes as examples and unite them in a single co-pilot UI using a worflow to control the processing sequence.

You will discover how to use AI Foundry Studio to build a project with a prompt flow orchestrating between different models and publishing and endpoint for the end user

Azure Open AI RAG and other architectures: From Accelerator to Prompt Flow and Agents

Open AI and RAG solutions in Azure environment can be very powerful and have many different implementation methods.

On this full day workshop we will start from basic explanation about RAG architectures using OpenAI with Azure AI Foundry, passing through the implementation of the ChatWithYourData Solution Accelerator and advancing to with Azure ML Prompt Flow to make orchestrations between RAG solutions and other AI solutions

We don't stop there: We will analyse the concept of assistants and finally advance to Agents using Azure AI Agent Service and Azure AI Foundry

You will discover the differences between models, flows, assistants and agents and the best scenario to use each one of them

Azure SQL and Data API Builder: Building Access to your Data

Data API Builder is a powerful development tool to help building CRUD solutions for our databases

Discover how easily CRUDs can be built using this powerful tool

Azure SQL and Open AI using T-SQL

Discover how to access Azure Open AI from Azure SQL, querying your data with natural language and opening a world of possibilities

Azure SQL to Fabric: 3 Real Time ingestion methods

Azure SQL works as a production relational database while Fabric is a data intelligence platform. We need to ingest and transform the data from production to data intelligence.

This ingestion process need to happen without creating a heavy load to production, otherwise this would affect production applications.

Fabric provides 3 different features to allow us implement this: Mirroring, CDC Ingestion and Fabric Databases.

Each one of these features have different purposes and benefits. The decison between which one to use relies on the modeling features we want to implement, such as the SCD type we want to apply to our dimensions.

In this session we will talk about the differences between these features and the best scenarios to implement each one. We will also cover the data modeling practices which requires us to choose one of these features.

Azure SQL to Fabric: 3 Real Time ingestion methods - Part 2

Azure SQL works as a production relational database while Fabric is a data intelligence platform. We need to ingest and transform the data from production to data intelligence.

This ingestion process need to happen without creating a heavy load to production, otherwise this would affect production applications.

Fabric provides 3 different features to allow us implement this: Mirroring, CDC Ingestion and Fabric Databases.

Each one of these features have different purposes and benefits. The decison between which one to use relies on the modeling features we want to implement, such as the SCD type we want to apply to our dimensions.

In this session we will talk about the differences between these features and the best scenarios to implement each one. We will also cover the data modeling practices which requires us to choose one of these features.

In the second part we will talk about the data modelling features which affect our method choice, highlighting the differences between the two mirroring features and the CDC Ingestion

Azure SQL Vector Support in AI Scenarios

Discover how the new vector support in Azure SQL can be used in AI scenarios.

The first stage of a RAG solution is Retrieval, the retrieval of documents related to the user question. Vector search alrorithms are the best to achieve this.

Discover how Azure SQL can be part of the Retrieval stage of a RAG solution

Building Multi-Agent Systems with Microsoft Agent Framework

Visual workflows take you far. But some agent systems are too complex, too dynamic, or too tightly integrated with existing code to be defined declaratively.

When the platform reaches its limits, Agent Framework is where you go next.

This session explores the code-first path to multi-agent orchestration. You will understand when Agent Framework is the right choice over Foundry's visual tools, and what that decision actually costs and gains in terms of control, flexibility, and complexity. From there, the session goes deep into the techniques — how agents are structured in code, how they communicate, how tools are integrated, and how multi-agent coordination is implemented programmatically.

The session closes with Hosted Agents: the production deployment target for everything you have built. Rather than managing containers and infrastructure yourself, Hosted Agents gives your code-first agent system a fully managed runtime with enterprise-grade identity, scaling, and observability — the bridge between local development and production.

Building Production-Grade AI Agents with Azure AI Foundry

This full-day advanced training focuses on building production-grade AI agents using Azure AI Foundry, starting from Model Context Protocol (MCP) and progressing toward a complete, intent-driven agent workflow. The session is practical and engineering-oriented, showing how real systems are composed rather than how isolated features work.

The day starts with Model Context Protocol as the foundation. Participants build an MCP server using Azure Functions, exposing tools and resources backed by a database. The MCP server is tested locally using the MCP Inspector, allowing participants to validate behavior, understand the contract, and debug issues in isolation.

Once the MCP layer is in place, an agent is created in Azure AI Foundry and linked to the MCP server. This section focuses on how agents invoke MCP tools and resources, how execution flows work, and how the same MCP endpoint can be reused by multiple agents. The same MCP server is then connected to Copilot Studio, demonstrating how Foundry agents and Copilot Studio copilots can share the same backend logic while providing different user experiences, including a small Copilot Studio demonstration.

The training then moves to knowledge bases, focusing on their real implementation through Azure AI Search. Knowledge bases are presented not as simple vector stores, but as hybrid retrieval systems combining vector search, keyword search, and semantic search. The session explains how a model controls the retrieval process, evaluates results, refines queries, and repeats searches when necessary to improve accuracy and grounding.

Knowledge bases are exposed through MCP endpoints, treating them as reusable services. Participants see how the same knowledge base can be consumed by both Azure AI Foundry agents and Copilot Studio through a consistent MCP URL, enabling predictable and repeatable retrieval behavior across tools.

The day concludes with a Foundry agent workflow that integrates both retrieval and structured data access in a single agent. The workflow identifies the user’s intent and routes the request accordingly, deciding whether to answer from the knowledge base (RAG backed by Azure AI Search), from the database exposed by the MCP server, or from a combination of both.

📚 Topics covered (table of contents)

🧩 MCP foundations (comes before agents)
➡ What MCP is and why it is the first building block
➡ MCP servers, tools, and resources
➡ Designing MCP endpoints for reuse and safety

🛠 Building an MCP server with Azure Functions
➡ Creating an MCP server
➡ Exposing database-backed tools and resources
➡ Local execution and debugging
➡ Testing endpoints using MCP Inspector

🧪 Testing MCP with MCP Inspector
➡ Installing the MCP Inspector
➡ Validating inputs and outputs
➡ Troubleshooting common MCP issues

🤖 Creating an agent in Azure AI Foundry
➡ Defining a Foundry agent
➡ Linking the agent to the MCP server
➡ Invoking MCP tools and resources
➡ Understanding execution flow

🔁 Reusing the same MCP backend in Copilot Studio
➡ Connecting Copilot Studio to the MCP server
➡ Small Copilot Studio demonstration
➡ Same backend, different user experience

📖 Knowledge bases backed by Azure AI Search
➡ Where knowledge bases are located
➡ Combining vector search, keyword search, and semantic search
➡ Model-controlled retrieval
➡ Query refinement and repeated search for accuracy

🔌 Exposing knowledge bases through MCP
➡ Knowledge bases accessed via MCP URLs
➡ Connecting knowledge bases to Foundry agents
➡ Reusing the same knowledge bases in Copilot Studio

🧠 Final build: intent-driven agent workflow
➡ Identifying user intent
➡ Routing between RAG (knowledge base) and structured data (database via MCP)
➡ Combining unstructured and structured responses
➡ Designing predictable and explainable agent behavior

Data Activator and the infinity notebook execution

What's the frequency of your data ingestions? How often is the data updated in your source?

In some situations, you may would like a continuos execution of the notebooks, either for ingestion or other reasons.

Discover how to achieve this using Data Activator

Chat with your Data with Azure OpenAI

AI is not the future anymore, it is the present. One challenging everyone is trying to solve is how to link your custom data with the powerful LLM models provided by OpenAI.

In this session you will discover how Azure OpenAI works together AI Search to build a powerful RAG architecture and how to use the Chat With Your Data accelerator provided by Microsoft which allows you to deploy a RAG architecture in a matter of minutes

Data Intelligence All-in-One : Synapse Analytics

An entire day of deep dive into Synapse Analytics, exploring the existing pools (serverless, dedicated and spark), usage scenarios and architecture, explaining from a data warehouse to virtual data lake and how this great tool also help in visualization scenarios, working very well together Power BI

Deciphering the Alphabet Soup: from AI Models to Agents

The AI world evolved very fast: Models, architectures, flows, assistants, agents and a lot more.

In this session, discover how these technologies relate to each other and identify the usage scenarios for each one, as well as the enduring effects these technologies bring to the software development scenario

Designing Intelligent Tool-Driven Agents

Modern Copilot Studio agents can do far more than answer questions—they can reason, act, collaborate with humans, and make controlled decisions.
In this session, we will build an agent step-by-step to demonstrate how Copilot Studio’s expanding toolset enables powerful, safe, and flexible automation scenarios.

We’ll walk through how to use each tool individually and then combine them into a coordinated workflow:

AI Prompt to generate dynamic content and chain reasoning

AI Approval to introduce governed decision points

Human-in-the-Loop to collect user input or expert confirmation

Adaptive output tools to shape responses

Other Copilot Studio tools that enable integrations, data actions, and workflow branching

By the end of the session, you’ll understand how to orchestrate these capabilities inside a single agent, enabling it to make automated decisions, request human oversight where needed, and produce high-quality, context-aware outputs.

Attendees will leave with a clear blueprint for constructing robust Copilot Studio agents that blend AI automation with responsible controls—ideal for enterprise use cases such as approvals, content generation, customer support, data processing, and more.

Deep Dive into Agents: Internals, Security and Multi-Agent Orchestration with Microsoft Foundry

Shipping an agent to production means answering questions most tutorials never ask. Where does your agent store its conversation history and uploaded files — in Microsoft-managed infrastructure, or in resources you own and control?

That choice — multi-tenant versus single-tenant — shapes your data residency, compliance posture, and security design from day one.

This full-day session opens with exactly those internals. You will understand how Foundry agents manage state, what single-tenant setup actually requires, and why it matters for enterprise environments.

From there, the session tackles security: how Foundry's built-in guardrails protect against jailbreaking and prompt injection at the model level, and how APIM adds a second, independent defence layer at the API gateway — enforcing content safety, rate limits, and governance before requests ever reach your agent.

The second half of the day shifts to orchestration. You will build multi-agent workflows visually in Foundry, then go code-first with Agent Framework — learning when platform tools are not enough and how to take full programmatic control over agent behaviour, tool integration, and multi-agent coordination. The day closes with Hosted Agents: deploying the full orchestration you built into a production-grade, managed runtime.

End to End Intelligent Document Automation Using Cognitive Services

Cognitive Services has many services to work with document recognition and help with document management and classification. Let's analyse some of these services and how they can be used together in a solution

Different performances of storagenprotocols

Wasbs: https; Abf; Many people may think they are all the same. Let's discover the difference

Eventhouse and Power BI: The Modelling Practices

A Kusto database in eventhouse poses different challenges for the modelling and visualization in Power BI

Query elements such as AGO and BIN are very difficult to translate from the Power BI visuals to a KQL query using direct query mode

Discover how to ensure the best performance when dealing with billions of rows and how power query dynamic parameters play an important role in this scenario

Kusto functions may also be essential for many scenarios and we will discover how to use them with dynamic parameters and direct query.

EventStream and EventHouse: Real-Time intelligence in Fabric

The real-time intelligence features in Microsoft Fabric provides what we need to build an entire lambda architecture in a single environment.

While in Azure Data Platform we need multiple resources and more complex configurations to achieve a real-time ingestion, in Fabric we can build everything using an EventStream to ingest the data and and EventHouse to store it.

The EventStream is capable to replace event hub and stream analytics and it's also compatible with Kafka API, working as a replacement for Kafka and allowing a quickly transition of existing applications.

Discover the many possibilities using these tools in Microsoft Fabric

Fabric and Security: Use Workspace Identity for Authentication

Fabric consumes, transforms and provide data. Security is essential in this scenario.

The authentication to data sources can be done in many different ways. Among different features, we can use workspace identities for authentication, or service accounts for authentication.

Let's discover how to make the authentication safe and easier combining these two features in a single authentication process.

Fabric and Data Mesh to control Santa toys production

It's very hard to the elves to control the production and distribution of Santa Claus's toys. It's time for Santa to help the elves to use Data Mesh in Microsoft Fabric to control the production

Fabric Data Pipelines: Making the orchestration faster with High Concurrency

Data Pipelines in Microsoft Fabric can be often used as an easy and low code method to orchestrate the execution of multiple notebooks.

However, they were very expensive. This is in the past, not anymore.

Using the High Concurrency configuration we can make notebooks orchestrations in data pipelines faster and less expensive

Fabric Hidden Gems: Advanced Lakehouse and Notebook Secrets for Real-World Impact

Fabric is broad — unifying storage, compute, data engineering, analytics, and machine learning. But beneath the surface lies a set of lesser-known, high-impact capabilities that can transform how you manage and optimize your data intelligence environment.

In this advanced session, we’ll uncover hidden gems across lakehouses, notebooks, and storage that are often undocumented or overlooked — yet crucial for data availability, performance tuning, and cost management. These are insights forged from real-world projects, and often missed by newcomers and even experienced users alike.

We’ll dive into advanced techniques, subtle behaviors of the platform, and smart architectural decisions you can apply immediately. You’ll walk away not only with concrete examples and live demos, but also a curated set of notebooks you can take home to explore and expand on the session’s discoveries.

Ideal for those already familiar with Fabric fundamentals and ready to level up their understanding, this session will give you the tools and perspective to unlock Fabric’s true potential.

Fabric Source Control: A diferent option for SQL Source Control

Lakehouses and Data Warehouses can contain many SQL objects, such as views.

We can manage these objects using a database project, it's possible, although there are still some limitations.

In some scenarios, this may be the best option. However, in other scenarios, it may be an overkill.

Let's talk about an alternate option to include SQL objects in Fabric source control

Fabric User Data Function: Write Back with Translytical Task Flows

Fabric User Data Function: Write Back with Translytical Task Flows

User Data Functions in Microsoft Fabric are so powerful and versatile that they’ve become known by many names. One of their most anticipated capabilities—Write Back—has finally arrived, unlocking an entirely new dimension in Power BI solutions.

This new feature, now rebranded with the dynamic title Translytical Task Flow, allows users to interact with and update data directly from their Power BI reports. It empowers us to go beyond analytics and truly close the loop between insight and action.

Among the many exciting use cases enabled by Write Back, one that stands out is the ability to capture unstructured contextual information—adding real-world nuance directly into your reports

Join this session to explore how User Data Functions and Translytical Task Flows can elevate your Fabric solutions. Discover practical scenarios, implementation tips, and the transformational potential this functionality brings to your data platform.

Fabric: The Secrets you need to know

We all know how broad Fabric is. All the Fabric experiences cover from data storage, queries, to Machine Learning.

These experiences have secrets. Details we should be aware of that make all the difference when managing a data intelligence environment.

From data availability to cost management, discover the hidden and sometimes undocumented secrets capable to make all difference in the management of the environment.

The secrets planned for this session focus mostly on lakehouse, storage and notebooks.

From Questions to Insights: Unlocking the Power of Fabric Data Agents

What if you could just ask your data and get instant, intelligent answers?

This session explores the many possibilities unlocked by Fabric Data Agents — the AI-powered interface that brings natural language to your data.

We’ll showcase a variety of usage scenarios, including:

- Creating agents in Fabric, Azure, and Copilot Studio
- Combining multiple data agents into a single intelligent assistant
- Enabling conversational analytics across your data ecosystem
- Connecting to diverse data sources with surprising flexibility

Get inspired by what’s possible and discover how Fabric Data Agents can elevate how people interact with data — from demos to architecture ideas.

From Code to Cost: Understanding Notebook Execution Models in Microsoft Fabric

Microsoft Fabric offers multiple ways to run notebooks — each designed for a different balance of speed, scalability, and cost efficiency. But what truly sets these options apart? And how can you decide which approach best fits your workload, your team, and your budget?

In this session, we’ll explore the key dimensions behind notebook execution strategies in Fabric — from interactive exploration to production-grade pipelines. You’ll learn how execution choices affect performance, how they interact with different data frameworks, and what these differences mean in practical and financial terms.

Through concrete examples and real-world comparisons, we’ll connect the technical mechanics of notebook execution with their business implications — helping you understand not just how they work, but when and why to use each.

By the end of the session, you’ll be equipped to make informed decisions about how to execute your notebooks in Microsoft Fabric with confidence and clarity — achieving the right balance between agility, efficiency, and cost.

From Source control to a full Lifecycle

In this double session, we will start explaining the source control features in Power BI and go deeper in relation about how to join source control features with deployment pipeline features.

The source control are already news for power bi developers, we will talk about how to adapt to it. The deployment pipeline is not new, but there are many challenges involves in maintaining a Fabric lifecycle - we will focus on them.

From Zero to Agent: Building AI Solutions with Microsoft Foundry

AI is no longer a research topic — it's a production engineering discipline. But before you can build, you need to understand the landscape: what AI actually is today, how it evolved from machine learning models into autonomous agents, and which Microsoft platform fits which problem.

This full-day session starts from the foundations and builds up to a working agent deployed to Microsoft Teams — entirely through the no-code and low-code capabilities of Microsoft Foundry and Copilot Studio.

You will learn when Copilot Studio is the right tool and when Microsoft Foundry is the better choice — a decision that is less obvious than it looks. You will then go hands-on: creating MCP servers using the Azure Function MCP Extension and using Data API Builder, grounding your agents with knowledge bases, and shipping those agents directly to Teams.

No prior agent experience required. By the end of the day, you will have built and deployed a real agent — and you will know exactly why every architectural decision was made.

Handling Multiple Schemas in Real-Time Streams with Fabric

Modern data platforms must ingest heterogeneous real-time data from multiple producers—each sending different schemas, versions, or malformed messages.

In this technical session, we’ll explore how Microsoft Fabric handles schema variability using schema inference, schema sets, and dynamic routing inside Real-Time Intelligence.

You’ll learn practical patterns for identifying incoming schemas, processing each one independently, detecting “bad messages,” and building a robust ingestion pipeline.

We will also demonstrate the new HTTP Endpoint feature for low-latency ingestion outside other traditional flows.

I'm migrating to Fabric - What now ?

Choosing Fabric as the platform for you company is only the first step.

The plans for a data intelligence platform requires much more. What architecture do you plan to use? Single Source of Truth? Data Mesh ?

Which techniques? ETL? ELT? Medallion ?

Are you going to use top-down or bottom-up ? Self-Service BI ? Self-Service data products ?

In this session we will analyse how all these methologies fit either together or as a choice according to your environment. Let's discover if your plans are complet or if there is anything missing.

Lakehouse Tables Optimization and Maintenance

Microsoft Fabric take a big step towards making data lakes acessible for all. But there are still many technical details which need to be handled to ensure the tables in the lakehouse are correctly optimized and the performance doesn't suffer due to some missing maintenance

Introduction: Why AI and Cloud

Discover a bit more about the importance of the adoption of AI and Cloud, how they can help your business and all that you will see during the conference

Make it fast: Analysing lakehouse performance in Fabric

When providing reports to end users, the lakehouse query performance is critical.

Some scenarios require the usage of direct query to be as close to real-time as possible to avoid the latency of an import mode semantic model and refreshs.

In these scenarios, analysing the performance of the queries arriving on the lakehouse to identify the most demanding ones and know where some fix is needed is crucial.

We will start from one of the most common causes of performance issue: lack of lakehouse basic maintenance, and proceed discovering how to analyse the query performance and stablished a performance baseline, comparing the before and after of a change made in the environment.

We will see how to make the query performance analysis, the information available to us and how to identify the source of the query, such as report and visual.

Make it fast: Automate query analysis in the lakehouse

Discover how to automate the creation of a baseline query performance in the lakehouse, reports to show the performance evolution and identify the source of the most demanding queries.

Fabric environment allow us to identify the report and visual responsible for each query. This is a kind of query lineage, allowing us to point exactly to where to fix when a query create problems in the lakehouse.

An automated system to extract query performance information allow us to keep track of the effects of every change made in our environment.

We can also identify the report and visual which are the source of the query using semantic kernel.

Mastering Microsoft Fabric Dev Lifecycle: From Solo to Team-Powered BI w/Source Control & Pipelines

Step into the new era of Power BI and Microsoft Fabric development, where team collaboration replaces solo work and source control drives efficiency. In this full-day hands-on session, you’ll learn how to:

🔗 Move from individual projects to team-powered BI by integrating your Power BI work with source control—from desktop to workspace.

🏗️ Set up and manage multiple environments (Dev, Test, Prod) using deployment pipelines to safely deliver new features and updates.

🛠️ Overcome real-world challenges that arise when multiple developers contribute to the same BI solution.

🌿 Implement branch-out patterns and collaborative workflows with Power BI and Azure DevOps.

🔄 Unify Power BI and Fabric development lifecycles for a seamless, automated, and team-ready delivery process.

Leave this session equipped with practical skills, ready-to-use patterns, and confidence to run BI projects collaboratively, turning solo efforts into team-driven success.

Mastering Connections in Microsoft Fabric: Security and Management Best Practices

Connections are at the core of Microsoft Fabric—whether you're ingesting data or enabling access for reports and semantic models. As Fabric evolves, so does the importance of securing and managing these connections effectively.

This session explores the full spectrum of connection capabilities, from user permission controls and workspace identity to service principal authentication. Learn practical strategies and best practices to enhance security, streamline management, and ensure robust governance across your Fabric environment.

Mastering Microsoft Fabric Dev Lifecycle: From Solo to Team-Powered BI w/Source Control & Pipelines

Step into the new era of Power BI and Microsoft Fabric development, where team collaboration replaces solo work and source control drives efficiency. In this full-day hands-on session, you’ll learn how to:

🔗 Move from individual projects to team-powered BI by integrating your Power BI work with source control—from desktop to workspace.

🏗️ Set up and manage multiple environments (Dev, Test, Prod) using deployment pipelines to safely deliver new features and updates.

🛠️ Overcome real-world challenges that arise when multiple developers contribute to the same BI solution.

🌿 Implement branch-out patterns and collaborative workflows with Power BI and Azure DevOps.

🔄 Unify Power BI and Fabric development lifecycles for a seamless, automated, and team-ready delivery process.

Leave this session equipped with practical skills, ready-to-use patterns, and confidence to run BI projects collaboratively, turning solo efforts into team-driven success.

MCP Server: The Phone Catalog for AI Agents

AI Agents took the models to another level, allowing them to execute actions, instead of only answering questions.

The next stage is the capability for the agent to discover which actions can be executed by itself, checking a kind of "catalog".

That's the MCP Server: A catalog of services which are actionable by AI models

You will discover easy ways to create MCP Servers using Azure Functions and use the tools we have available to test them.

Finally, you will discover how to use MCP servers to create agents in Copilot and Azure Foundry

MCP Server: The Phone Catalog for AI Agents

AI Agents took the models to another level, allowing them to execute actions, instead of only answering questions.

The next stage is the capability for the agent to discover which actions can be executed by itself, checking a kind of "catalog".

That's the MCP Server: A catalog of services which are actionable by AI models

You will discover easy ways to create MCP Servers using Azure Functions and the tools we have available to test them.

Finally, you will discover how to use MCP servers to create agents in Copilot and Azure Foundry

Microsoft Fabric end to end

Microsoft Fabric is still in preview but everyone agrees about the great revolution it brings to our enterprise data platform.

From technical concepts to architecture recommendations, discover how Microsoft Fabric works.

Memory Grant Feedback: The hidden improvements

Discover how memory grant feedback is one of the features capable to provide improvements out-of-the-box when you upgrade from old SQL Server versions to the most recent ones

Microsoft Foundry Demystified: Building and Evaluating Agents with MCP and Guardrails

Jump into the new Microsoft Foundry ecosystem and learn how to build your first AI-powered workflow.

This session breaks down the essentials—from what Foundry is and how it works, to creating an MCP server as an Azure Function and connecting it to an agent.

We’ll also explore how Foundry evaluates agent performance and how to implement guardrails to keep your automations safe, reliable, and aligned with organizational requirements.

A fast-paced and practical introduction for anyone eager to start building with Foundry.

Microsoft Fabric: Azure SQL Mirroring and CDC - how to choose

Azure SQL Mirroring to Microsoft Fabric and CDC Ingestion are two different methods to ingest data in real time from Azure SQL to Fabric.

In this session you will discover how both methods work, what are the differences between them and how to choose which one to use for each kind of problem.

Orchestrating AI Agents: Multi-Agent Workflows with Microsoft Foundry

A single agent can answer questions and take actions. But real enterprise automation rarely fits in a single agent.

Complex business processes need agents that coordinate — passing context, splitting work, waiting for approvals, and converging on results. That is where workflows come in.

This session dives into how Microsoft Foundry approaches multi-agent orchestration through workflow agents — the declarative, visual layer that lets you connect agents into coordinated pipelines without writing orchestration code.

You will understand the architecture behind workflow agents, how they manage state and context across steps, and how to design workflows that are repeatable, observable, and maintainable.

The session also draws a clear boundary: where visual orchestration is the right tool, and where its limits push you toward code-first approaches.

Monitoring Fabric with Data Activator and Real Time

Data Activator and Real-Time Intelligence as a whole have what we could call "meta-features" : They can be used to monitor the Fabric environment.

Using events in Data Activator and Eventstreams in RTI we can monitor notebooks and pipelines executions and ensure our intelligence environment will be always up and running

Picking the Right Azure SQL Deployment

Azure offers a rich ecosystem for running SQL Server in the cloud, spanning PaaS and IaaS options—from fully managed Azure SQL Database and SQL Server Managed Instance to SQL Server on Virtual Machines. But with so many options and SKUs available, it can be challenging to know which service fits your workload, budget, and operational requirements.

In this comprehensive session, we’ll guide you through everything you need to know about SQL Server in the cloud:

☁️ Deep dive into available SKUs and tiers – understand the differences, capabilities, and limitations of Azure SQL Database, Managed Instance, and SQL Server on VMs.

⚖️ Decision-making frameworks – learn how to choose the right deployment model based on workload patterns, scalability needs, and operational complexity.

Whether you’re responsible for database architecture, cloud operations, or cost optimization, this session will equip you with the knowledge and practical guidance to make informed decisions and maximize the value of SQL Server in the cloud. Don’t just run SQL Server in Azure—run it smarter, safer, and more efficiently.

Performance and Query Engine Improvements in SQL Server 2025

SQL Server 2025 raises the bar for query performance and troubleshooting with a new set of developer- and DBA-friendly features. This session dives into improvements that directly impact how you design, run, and tune workloads:

Optional Parameter Plan Optimization — reduce parameter sniffing issues without rewriting code, giving queries more stable and predictable performance.

ABORT_QUERY_EXECUTION Hint — finally stop runaway queries gracefully, protecting system resources without needing manual intervention.

Cardinality Estimator (CE) Feedback for Expressions — let the optimizer learn from mistakes in estimating row counts, leading to smarter execution plans over time.

Columnstore Enhancements — faster analytics with less overhead, ensuring large fact tables and reporting workloads keep up with demand.

Time-Bound Extended Events Sessions — capture what you need, when you need it, without bloated traces that run forever.

Query Store Improvements — better visibility and fine-grained control over execution plans, making it easier to diagnose and stabilize workloads.

This isn’t just a list of new knobs — it’s a toolkit for making SQL Server “smarter” on your behalf. You’ll learn how each feature works, when to apply it, and how it can reduce firefighting time while improving both performance and stability.

If you manage or tune SQL Server workloads, this is the session where you’ll see what 2025 delivers for the day-to-day performance battles that matter.

Power BI Hybrid Tables

Discover how to use Hybrid Tables to achieve a solution with incremental load and real time data at the same time, and how dataset tables partitioning works

Power BI Architecture

In this training day you will discover the architecture resources available in Power BI, such as composite models, aggregations, incremental refresh, hybrid tables, dataflows, datamarts and more

Protecting Santa's database against the bad kids

Bad kids always try to break Santa's database and insert fake gift requests. Discover the network secrets to ensure the security of the Azure SQL Databases

RAG in the Era of Knowledge Bases: Building Intelligent Retrieval with Microsoft Foundry

In the year of the Agents, the industry is shifting its attention to autonomous workflows and orchestration—but Retrieval-Augmented Generation (RAG) remains just as essential as ever. What’s changing is how RAG is built and how it integrates with new technologies and APIs, especially Microsoft Foundry.

This session explores how to design modern RAG architectures using Foundry, clarifying the roles of both AI Search indexes and the new Knowledge Base concept introduced together with AI Search. You’ll learn when a simple vector-based index is enough, when a Knowledge Base becomes necessary, and how Knowledge Bases work internally to enhance grounding and retrieval quality.

We’ll also demonstrate how agents can use either approach—whether linking directly to an AI Search index or leveraging a Knowledge Base as a richer grounding source. By the end, you’ll understand how to choose the right RAG pattern for your scenario and how Foundry elevates retrieval in agent-driven systems.

RAG Solutions: From Accelerator to Prompt Flow

RAG solutions in Azure environment can be very powerful and have many different implementation methods.

On this session we will start from basic explanation about RAG architectures using OpenAI, passing through the implementation of the ChatWithYourData Solution Accelerator and concluding with Azure ML Prompt Flow to make orchestrations among many pieces of data science solutions

Real Time Intelligence in Fabric: From Eventstreams to actions with Activators

In this full day training the attendee will discover why Fabric is very powerful for real time ingestion.

Using a tool capable to build an entire lambda architecture in a single environment, we will illustrate the different capabilities and also answer key questions.

Eventstream is capable to replace Azure Event Hub and Stream Analytics. It allow us to build multiple different ingestion architectures and it's also compatible with Kafka API, allowing an easy migration of applications using Kafka

Discover why is Kusto Databases in eventhouses are better for real time than other options. The many policies a kusto database has, such as ingestion, update and retention, besides the powerful materialized view feature.

Real Time Dashboards are perfect to provide visualization over the incoming data, while Activator allow us to trigger actions from data events and even more.

RTI with Fabric: Kusto, dashboards and activator

After building the eventstream, which is one of the main pieces of a lambda architecture, it's time to advance to the next stages.

Kusto Database in Fabric has many policies capable to help during the ingestion process, reducing the amount of work we need to execute.

Dashboards come immediatelly after, enabling us to display the real time information as it arrives.

Will you look to the dashboards 24 hours a day, or would you like your system to react to specific facts happening in your data?

That's where Data Activator comes into the architecture. We can trigger actions from the real time data, automatically reacting to what's happening.

Santa’s Command Protocol: Building the North Pole MCP Server

In the heart of the North Pole’s data workshop, Santa’s engineers are rethinking how he interacts with his vast Naughty and Nice database. Each December, millions of wish records arrive, and Santa needs a faster, more natural way to ask questions like “Who still needs a gift in Milan?” or “How many bicycles are pending assembly?”

This year, the team introduces the Merry Command Protocol (MCP)—a layer that bridges natural language queries and structured data access. Using Azure Functions, the engineers define callable methods that connect directly to Santa’s MSSQL database. Turning each function into an MCP tool, the Azure Function can be linked to Copilot Studio as an MCP server, allowing Santa to query the database conversationally, without writing SQL or calling stored procedures.

The session walks through how to design and expose these MCP tools, configure the Azure Function endpoints, and connect them to Copilot Studio. By the end, attendees will see how to enable any Copilot to query operational data safely and intelligently—just like Santa consulting his lists before takeoff.

Scaling Smart: Best Practices for Pushdown and Distributed Processing in Microsoft Fabric

Efficient data processing isn’t just about writing code that works — it’s about writing code that scales. In Microsoft Fabric, the difference between a query that flies and one that drags often comes down to how effectively the work is distributed across the cluster and pushed down to the underlying engine.

This session dives into the core principles of scalable data development, revealing the key practices that ensure your transformations and queries run where the data lives, not just where the code sits. We’ll discuss how to think about pushdown logic, how distribution impacts performance, and what design patterns separate efficient pipelines from costly ones.

Expect a clear, practical exploration of what truly happens under the hood when Fabric distributes work — and how you can influence it through your code, your data model, and your execution strategy.

By the end, you’ll walk away with a set of actionable best practices to make your Fabric workloads faster, cheaper, and more reliable — scaling intelligently across the full power of the platform.

Save money with High-Performance pipelines

Discover how to save money and improve your Fabric data pipelines using the High-Performance feature for notebooks execution inside a data pipeline

Securing AI Agents: Architecture, Guardrails and API Gateway Protection with Microsoft Foundry

Security for AI agents starts before the first line of code. The first decision — where your agent stores its conversation history and uploaded files — is already a security decision.

Choosing between single-tenant and multi-tenant infrastructure determines your data residency, your compliance boundaries, and how much control you retain over what your agents know and remember.

This session builds from that foundation outward. You will understand what single-tenant agent setup actually requires, why multi-tenant is the faster path but not always the right one, and how to make that call for enterprise environments.

From there, the session moves into runtime threats. You will learn how Foundry's built-in guardrails protect against jailbreaking and prompt injection at the model level — and why that is not enough on its own. APIM adds a second, independent defence layer at the API gateway, enforcing content safety, rate limits, and governance before requests ever reach your agent. Two layers, two different threat surfaces, one coherent security architecture.

Secrets of Fabric Capacity Consumption

Discover many secrets about Microsoft Fabric capacity consumption. How to understand the Fabric Capacity Metrics, what are the limitations about changing capacities, shortcuts and consumption and much more

Single Source of Truth Architecture on Microsoft Fabric

Microsoft Fabric has a very structured and PaaS method of work which makes the needs of a single source of truth architecture not so obvious to be fulfilled.

Let's talk about possible ways to build a single source of truth architecture in Microsoft Fabric, supporting the needs of an Enterprise Data Platform.

Smarter T-SQL in SQL Server 2025: Regex, Fuzzy Matching, and Beyond

SQL Server 2025 introduces a wave of developer-focused enhancements that make T-SQL more expressive, powerful, and practical. In this session, we’ll explore:

Native Regular Expressions — simplify complex text parsing and validation directly in T-SQL without workarounds.

String Concatenation Enhancements — faster, cleaner syntax for building dynamic text, JSON, or CSV results.

Fuzzy Matching Functions like EDIT_DISTANCE, EDIT_DISTANCE_SIMILARITY, and Jaro family functions — designed for cases where spelling errors, typos, or formatting variations matter more than meaning. These shine in tasks such as matching customer names, detecting near-duplicates, or cleaning messy imports.

OPENROWSET Improvements — easier access to external files and formats, making integration steps more straightforward.

We’ll also tackle an important distinction: when to rely on syntactic similarity (fuzzy string functions) versus semantic similarity (vectors). Vectors excel at meaning, but often require extra infrastructure, storage, and indexing. The new string functions are lightweight and efficient, perfect for scenarios where you only need to catch a typo, misspelling, or formatting inconsistency.

Through practical demos, you’ll see how these features simplify everyday tasks that developers and DBAs face: cleaning messy data, validating inputs, or reconciling inconsistent records. By the end, you’ll leave with a toolkit of new techniques to solve problems faster and more elegantly — all without leaving T-SQL.

SQL Endpoint Secrets you need to know

SQL Endpoint is an object used in many Fabric objects. Lakehouse and Data Warehouse are only two of them.

This object has secrets ignored by many Fabricators. The fact this object is serverless and uses a cache, for example.

In this short session you will discover how these secrets work and how they affect our data intelligence implementations.

We will also talk about how we may need to workaround some issues.

SQL Server 2019 Big Data Cluster

SQL Server BDC is a Data Lake House solution you can implement on premise or in the cloud, allowing you to use a single tool to achieve a data lake house, working with structured and unstructured data

SQL Server BDC: Queries over Data and Spark Pool

On this session you will discover how queries executed from the master node to the data pool or spark pool are processed, how the queries are distributed among the pool nodes and how polybase gets involved in the process

SQL Server In The Cloud

Managing SQL Server in the cloud is not the same as on premises. On this full day pre-con you will discover the differences, starting from the available options in the cloud and going deep on many management differences on the cloud services

SQL Server TempDB: Evolutions up to Concurrent GAM and SGAM

Discover how TempDB evolved its access up to SQL Server 2022 to concurrent GAM and SGAM.

Each SQL Server version brings new evolutions to TempDB. The knowledge of these features can ensure you will know exactly when you need to make special configurations to your environment

SQL Server: Tables, Files and FileGroups secrets

On each version of SQL Server, small details evolve. Configurations that before were only on trace flags, only on server level, become available in statements, on file or filegroup level and had their default option changed.

Sometimes the subject is so complex that it's better to say "Just run faster". Let's dig into details of some of these changes.

Taking Control: Building Deterministic Workflows for Multi-Agent Orchestration in Microsoft Foundry

Agents bring powerful but inherently non-deterministic behavior, which can make full solutions unpredictable if left entirely in their hands. Microsoft Foundry workflows provide the structure needed to guide that intelligence—ensuring a controlled, repeatable path while still leveraging the flexibility of agent-based reasoning.

In this session, we demonstrate how a workflow can orchestrate multiple agents with different problem-solving approaches. Starting from an intent-identification step, the workflow evaluates the user’s request and then routes execution to the agent best suited for the task. This pattern shows how workflows impose clarity and governance on top of non-deterministic components, enabling reliable multi-agent solutions without sacrificing adaptability.

By the end of the session, you’ll understand how workflows bring order, transparency, and operational control to agent-based systems in Foundry—allowing you to combine diverse agents within a predictable execution flow.

Talk to Your Data: Natural Language Queries with SQL MCP and Microsoft Foundry

What if anyone in your organisation could query a database just by asking a question — no SQL, no waiting for the data team?

That's what this session is about. We'll walk through how easy it is to build a SQL MCP server, connect it to an agent in Microsoft Foundry's new UI, and start getting answers from real data using plain language. We'll cover different approaches to building the SQL MCP so you can pick what works for your setup, and we'll touch on guardrails to keep things production-safe.

The Foundry UI has made this kind of agent development far more accessible than it used to be. Come see how quickly you can go from zero to a working natural language data assistant.

The Elf Assistant Project: Extracting Wish Details with Azure Function AI Bindings

Every December, the North Pole mailroom receives millions of letters written by children from every corner of the world. Before these wishes can be processed, the elves must identify the sender, extract the requested toy, and determine the delivery address—a repetitive, text-heavy task that slows the entire operation.

This year, the elves are getting help from Azure Function AI bindings, which make it possible to use natural language models directly within their data ingestion pipeline. Each letter is passed to an AI-powered function that automatically extracts three key details: the child’s name, the toy requested, and the delivery address. The results are returned as structured data, ready to be inserted into the Naughty and Nice database.

The session demonstrates how to use AI bindings to process unstructured text inputs with minimal code, define output schemas, and manage prompt behavior for consistent extractions. While this approach doesn’t replace Santa’s document intelligence systems, it shows how lightweight AI models embedded in event-driven functions can quickly enrich incoming data and keep the workshop running smoothly through peak season.

Temporal Tables: Keeping data history

Discover how temporal tables in SQL Server and Azure SQL and keep the data history and retrieve for you how the data was in a specific date.

Tempora tables are a great automatic method to keep the history of your production data.

The Evolving Drop Zone in Microsoft Fabric: From File Triggers to Continuous Ingestion

Drop Zones play a key role in data ingestion pipelines—especially when working with file-based sources. In Microsoft Fabric, support for Drop Zone architectures has expanded significantly, offering new levels of flexibility and automation.

This session will walk through the evolution of Drop Zone capabilities, from simple file triggers and Azure Blob Storage event capture to Fabric-native file events and continuous ingestion. Explore the architectural options available today and learn how to design efficient, reliable Drop Zones that fit your data flow needs.

The Nearly No-Code Medallion: Fabric MLV Meets Shortcut Transformations

Discover how Microsoft Fabric revolutionizes medallion architecture implementation by combining two powerful low-code features: Materialized Lake Views (MLV) and Shortcut Transformations.

This session demonstrates how to build production-ready bronze-to-gold pipelines with minimal coding while maximizing efficiency and data quality.

Shortcut Transformations automate your bronze layer by continuously syncing raw files (CSV, JSON, Parquet) from external sources into Delta tables—checking for changes every 2 minutes.

Say goodbye to complex ingestion pipelines: simply point to your data source, configure transformations, and watch your bronze layer stay automatically synchronized.

Materialized Lake Views then take over, transforming this raw data through declarative SQL statements that automatically manage your silver and gold layers. MLVs provide intelligent refresh strategies (incremental, full, or skip), built-in data quality constraints, and automatic dependency management—turning what used to require complex orchestration into simple SQL definitions.

Together, these features create a nearly no-code medallion architecture where:

- Bronze ingestion happens automatically via shortcuts with file transformations
- Silver cleansing and enrichment uses MLV with SQL-based validations
- Gold aggregations leverage MLV lineage for correct execution order
- The entire pipeline self-orchestrates based on data changes

You'll learn the internals of both features, understand their limitations and ideal use cases, and see live demonstrations of building a complete medallion architecture. Walk away with practical patterns for combining shortcuts and MLVs to modernize your data engineering workflows while reducing development time by up to 70%.

Key Takeaways:

- Set up automated bronze layer ingestion using shortcut transformations
- Build declarative silver/gold transformations with Materialized Lake Views
- Implement data quality rules directly in your transformation logic
- Understand when to use MLV vs. traditional notebooks
- Optimize refresh strategies for cost and performance
- Navigate limitations and combine both features for maximum impact

The Real-Time Data Journey in Microsoft Fabric: From Eventstream to Automated Actions

Unlock the full potential of Microsoft Fabric for real-time analytics and decision-making in this full-day training. Learn how to design, build, and operate end-to-end real-time intelligence pipelines—from raw event ingestion to live dashboards and automated actions.

You will discover how to:

🚀 Ingest streaming data with Eventstream: Replace Azure Event Hub and Stream Analytics, build multiple ingestion architectures, and leverage Kafka compatibility for easy migration.

🏗️ Design a lambda-style architecture in a single environment: Understand how Microsoft Fabric simplifies the complexities of real-time systems.

📊 Create and optimize Kusto Databases in eventhouses: Explore ingestion, update, and retention policies, and learn how materialized views dramatically improve query performance and dashboard responsiveness.

🖥️ Build real-time dashboards: Visualize incoming data as it arrives and gain actionable insights instantly.

⚡ Automate reactions with Data Activator: Trigger workflows, alerts, or downstream processes directly from your real-time data—eliminating the need to monitor dashboards 24/7.

🧩 Combine materialized views with Power Query dynamic parameters: Enable intelligent, user-driven filtering while benefiting from pre-aggregated, high-performance data.

Through practical demos, architectural guidance, and best practices, you’ll leave this session with the knowledge to design, deploy, and operate real-time intelligence solutions in Fabric—turning streaming data into meaningful actions faster than ever before.

The North Pole Query Engine: When Copilots Meet MCPs

In the heart of the North Pole’s data workshop, Santa’s engineers are modernizing how the elves query the Naughty and Nice database.

The team experiments with multiple approaches to turn natural questions into precise T-SQL—either through the structured orchestration of an MSSQL MCP Server or with a Copilot guided by a carefully crafted system prompt.

Their mission is to understand the nuances between these methods and explore how combining them can deliver the best balance of performance and cost efficiency, ensuring that more of the budget goes toward toys instead of model usage.

As the Christmas deadline approaches, the workshop races to achieve true natural language processing efficiency—so every child’s wish can be matched, validated, and delivered right on time.

Tracking Rudolph: Real-Time Data at the Speed of Christmas

When Santa takes off, real-time insight becomes essential. The North Pole’s operations center must track Rudolph’s location, confirm each child’s delivery, and update toy inventory the moment a gift is dropped off.

This year, the sleigh’s telemetry feeds a Microsoft Fabric Eventstream carrying three critical pieces of data: the current geo-position, the child’s unique ID, and the gift’s unique ID. As events stream in, Fabric processes them instantly—updating a live tracking map, decrementing toy stock levels, and counting how many children have received their gifts.

In this session, we’ll build this real-time architecture step by step, from ingesting event data to visualizing it on a live dashboard. Attendees will learn how to use Fabric Eventstream, Lakehouse integration, and Real-Time Dashboards to achieve sub-second analytics for operational intelligence. It’s a hands-on look at real-time data pipelines that can handle Christmas Eve scale—and keep the sleigh flying on schedule.

Unlock Real-Time Data with SQL Server 2025 Change Event Stream and Microsoft Fabric

The days of waiting for batch ETL are over. With SQL Server 2025, the new Change Event Stream feature lets you capture every insert, update, and delete in real time and publish them directly as an event stream. Combined with Microsoft Fabric, this unlocks a powerful new way to build low-latency, event-driven data solutions without the overhead of traditional pipelines.

In this session, we’ll dive into the architecture and inner workings of Change Event Stream, highlighting how it differs from Change Data Capture and Change Tracking. You’ll learn how to connect SQL Server 2025 to Fabric Eventstream, Lakehouse, and Real-Time Analytics to:

⚡ Stream operational data continuously into Fabric with minimal lag
📊 Build real-time dashboards and alerting systems that react instantly to business events
🔄 Reduce complexity by replacing polling-based ingestion with push-based streaming
🚀 Enable modern data apps that bridge transactions and analytics seamlessly

Join us to see how SQL Server 2025 and Fabric together transform raw transactions into instant insights, giving your organization the agility to act in the moment.

Turbocharged Insights: Kusto Materialized Views and Dynamic Filtering in Power BI

This session explores how to harness Kusto’s materialized views to dramatically improve query performance and report responsiveness in Microsoft Fabric. Starting from the fundamentals, we’ll cover how materialized views are created, managed, and maintained — including strategies for aligning retention with source tables, managing backfill, and querying both materialized and non-materialized data seamlessly.

We’ll then zoom into eventhouses and show how materialized views fit into its architecture. Finally, we’ll bring it all together by showing how to combine materialized views with Power Query dynamic parameters in Power BI — enabling intelligent, user-driven filtering while still benefiting from pre-aggregated, optimized data.

Expect practical demos, architectural tips, and design patterns to help you build fast, scalable analytics solutions using Kusto and Power BI in Fabric.

Unlocking Business Agility: How T-SQL Can Trigger and Integrate with Cloud Services

In modern enterprises, data alone is not enough—actionable insights and automated workflows are what drive business value. This session explores how Azure SQL can become a central orchestrator by communicating directly with REST APIs. Using T-SQL as the starting point, attendees will see how to integrate SQL workloads with cloud services, internal business APIs, and serverless platforms like Azure Functions and Microsoft Fabric.

We’ll cover practical, real-world scenarios where SQL can trigger external services to:

🚀 Automate routine tasks such as data validation, enrichment, or reporting
🔗 Integrate with corporate systems for inventory checks, approvals, or alerts
⚡ Access specialized compute capabilities without leaving the SQL environment

Security, governance, and scalability are key concerns in enterprise integrations. The session demonstrates how to leverage Azure API Management (APIM) as a proxy and when this is needed.

Attendees will walk away with a hands-on understanding of:
📝 Calling Azure Functions and Fabric UDFs from T-SQL for serverless orchestration
🔒 Using APIM to stay flexible and secure
💼 Practical business applications: real-time notifications, validation services, and decision-support workflows

This session is designed for data professionals, database developers, and architects who want to bridge the gap between SQL workloads and modern cloud services, unlocking automation, efficiency, and actionable insights directly from Azure SQL

Unlocking Marketing Intelligence with AI Transformations in Fabric

🔹 How can we turn raw customer data into actionable marketing intelligence — quickly and efficiently?
🔹 In this session, we’ll build a ## marketing solution for review sentiment analysis, showcasing how Fabric Shortcut Transformations and AI Shortcut Transformations work in practice.

You’ll see how to:

Simplify complex data pipelines using shortcuts ⚡

Apply AI directly within your platform to extract sentiment 🤖

Turn everyday marketing data into actionable insights 📊

Gain transferable skills to apply these techniques to other projects 🚀

👉 Whether you’re a data professional or marketing innovator, this session will show practical ways to leverage Fabric shortcut transformations and shortcut AI transformations to unlock business value.

Unlocking External Systems in Copilot Studio with MCP Servers

Learn how Microsoft Copilot Studio can securely connect to any external system using the Model Context Protocol (MCP).

In this session, we’ll walk through how MCP servers expose tools to copilots, how Studio consumes these capabilities without custom plugins, and how to design reliable, typed, and governed integrations.

You’ll leave with a practical understanding of MCP fundamentals and a working pattern you can adopt immediately in your organization.

Using Delta Lake with Synapse

Delta lake is a storage layer that provides us many advantages when dealing with Data Lake Storage. This storage layer is available for us in Synapse.

Discover the benefits of this great storage feature and how to use it in Synapse Analytics

Unpacking AI in Azure: Features, Architectures, and Best Practices

Every day, new features, architectures, and technologies emerge, rapidly enhancing AI capabilities. As AI evolves, understanding how its components fit together is crucial for designing efficient, scalable, and impactful solutions.

In this session, we will explore the key AI features available in Azure, breaking down essential concepts such as models, model training, grounding, prompts, assistants, flows, and agents. We will discuss how these elements interact, when to use each, and how they contribute to building intelligent applications.

We'll dive into the lifecycle of AI models—from selecting pre-trained models to fine-tuning and deploying custom solutions. You'll learn how grounding techniques enhance AI reliability by incorporating enterprise knowledge, and how prompt engineering can optimize AI interactions. We’ll also cover the role of assistants, orchestrated flows, and autonomous agents in automating workflows and decision-making processes.

By the end of this session, you’ll have a clear understanding of how Azure AI services work together, enabling you to choose the right tools and architectures for your AI-driven solutions. Whether you're working with conversational AI, automation, or advanced data-driven decision-making, this session will provide practical insights to help you navigate the rapidly evolving AI landscape.

Varchar(max) secrets: What you should know about LOB storage

varchar(max) is easy to use but it has consequences many DBAs may not be aware about.

Let's understand how Large OBjects are stored in SQL Server to fully understand these consequences

Vectors & AI: The Twin Engines of SQL Server 2025

Unlock the full potential of SQL Server 2025 as it brings together vector support and AI model integration to revolutionize how you query and understand your data. In this session, you’ll discover how vectors enable semantic understanding—moving beyond traditional text search to interpret the meaning of your queries and retrieve truly relevant results.

We’ll explore the enhanced AI model integration, making it easier than ever to call large language models directly from SQL Server, contextualize query results, and enrich your applications with intelligence—without compromising security. Learn how the latest improvements simplify development compared to previous Azure SQL features, while giving you the power to combine vector similarity and LLM-driven insights in a single query.

Whether you want to perform semantic search, generate AI-assisted summaries, or contextualize analytics, this session shows you how SQL Server 2025 makes advanced AI and vector operations fast, secure, and developer-friendly.

Why Your Fabric Lakehouse Gets Slower Over Time (and How to Fix It)

This full-day advanced training focuses on keeping Microsoft Fabric Lakehouses performant, cost-efficient, and predictable over time. The session is aimed at engineers and architects who already use Fabric and want to understand what actually happens under the hood, why performance degrades, and how to fix it systematically at scale.

The day starts with a detailed, practical explanation of how the Delta format works in Fabric. This includes how data is written, how the transaction log evolves, how small files and fragmentation are created, and why these mechanics directly impact query latency and capacity consumption. Rather than abstract theory, the focus is on understanding the concrete consequences of common ingestion and update patterns.

Building on this foundation, the training explains why OPTIMIZE and VACUUM are required, what problems they solve, and how to use them safely. Attendees learn what OPTIMIZE actually does to files, what VACUUM removes, how retention works, and how poor maintenance strategies can easily increase cost instead of reducing it.

The scope then expands from a single lakehouse to an enterprise perspective. Participants learn how to design and implement a centralized maintenance approach that works across multiple workspaces and lakehouses, avoiding ad-hoc notebooks and inconsistent practices. The session covers parameterized maintenance jobs, scheduling strategies that do not interfere with interactive workloads, and governance patterns to ensure maintenance is applied consistently across the organization.

A significant portion of the day is dedicated to performance diagnostics using lakehouse system tables. Attendees learn how to extract performance and workload signals, identify heavy queries, and map internal identifiers back to meaningful objects such as semantic model names and report names. This makes it possible to understand which reports and models are driving load and to turn performance troubleshooting into a repeatable process.

The training also covers critical lakehouse configuration choices that materially affect performance and cost. In particular, it explains V-Order in detail, including what it changes, why it is no longer a default decision, and how to plan where it should and should not be applied. Attendees learn how to validate V-Order impact and avoid blanket configurations that lead to unnecessary compute usage.

The day concludes with table design and query execution practices that complement maintenance activities. This includes partitioning strategies, Z-Order usage for specific access patterns, and practical PySpark query-writing techniques that avoid common performance pitfalls in Fabric. These topics tie together storage layout, maintenance, and query behavior into a single, coherent performance strategy.

📚 Topics covered (table of contents)

🧱 Delta format internals in Fabric
➡ Delta transaction log structure and lifecycle
➡ What different operations write to storage
➡ Small files, fragmentation, and performance decay
➡ Relationship between storage layout and query cost

🧹 OPTIMIZE and VACUUM: purpose and correct usage
➡ What OPTIMIZE actually changes at file level
➡ What VACUUM removes and how retention works
➡ Safe execution patterns and scheduling
➡ Common maintenance anti-patterns that increase cost

🏢 Enterprise-scale lakehouse maintenance
➡ Designing a centralized maintenance strategy
➡ Applying maintenance across multiple lakehouses and workspaces
➡ Parameterization and reuse of maintenance logic
➡ Scheduling without impacting interactive workloads
➡ Governance, standards, and observability

📈 Performance diagnostics with lakehouse system tables
➡ Extracting workload and query metrics
➡ Identifying heavy and recurring queries
➡ Mapping internal IDs to semantic models and reports
➡ Building actionable views for governance and capacity planning

⚙️ Critical configuration decisions (V-Order and beyond)
➡ What V-Order changes and why it requires planning
➡ Deciding where V-Order makes sense and where it does not
➡ Measuring and validating performance impact
➡ Avoiding configuration-by-default mistakes

🗂 Table design and query performance practices
➡ Partitioning strategies aligned with access patterns
➡ Z-Order usage and limitations
➡ PySpark query patterns that improve performance
➡ Common syntax and design mistakes that trigger expensive plans

Low Code Medallion: Using Lakehouse Materialized views

Materialized lake views are a gaming changing feature for lakehouses and medallion architecture creation.

They transform the creation of a medallion architecture in a low code task, creating big change possibilities for the data engineering work.

The feature internals has it's limitations, defining what scenarios it work for and what scenarios it doesn't.

Discover how this new feature works, their internals and how to build the materializade lake views .

You will find how to use this feature to get your pipelines and medallion architecture closer to become a no code one.

Agentic AI: Creating Agents in Azure

You will discover how to user Azure Agent Service to create an AI Agent. During the process, you will discover the differences between a model, a flow, an assistant and an agent.

The difference between all these AI resources can be subtle and lots of people mix these names. Identifying the difference between these elements will allow you to make a better decision about what to use in each situation

Fabric Databases and AI: Using vector search in Fabric Databases for AI architectures

Fabric Databases are based on Azure SQL Databases.

The support for vector search is included in both and it's a powerful feature to build AI architectures, either RAG or similar architectures depending on vector search features

Using HTTP calls from T-SQL and vector search features in SQL we can store embeddings inside Fabric Database. This provides a considerable improvement in text search and a potential integration between database solutions and Open AI solutions.

The embeddings stored in Fabric databases can be used as input for Open AI models with the benefit of the flexibel search of a relational database

From Azure to Fabric: Migrating to Fabric Databases

Discover how Fabric Databases work and when they should be used in comparison with other solutions such as Fabric mirroring.

Let's compare Fabric Databases with Azure SQL and understand when each one should be used. We will also analyse the differences between using a Fabric Database or mirroring Azure SQL to Fabric.

Discover how Fabric databases create a great benefit to analytics work by isolating the production and analytical process automatically, as long you correctly use the endpoints provided by Microsoft Fabric

Materialized Views: Real-Time Data Granularity in Eventhouses

Materialized Views in Kusto databases are an important feature to provide multiple data granularity levels for the real-time ingested data.

The management of materialized views in Kusto is not the same as in other database systems. The management of backfilling and retention policies are specially important in the views.

You will also discover how critical the query used in the view can be. The query can poses a considerable impact in the server performance depending on how it's designed.

Let's discover the tricks and best practices for materialized views

5 Kusto Secrets No one told you before

Real Time ingestion usually focus on the Evenstream work and the Eventhouse or Kusto database are only the repository for the data.

Kusto has many secrets which can help a lot during the process of real time ingestion. The many different policies we can apply over the imgestion process and tables can help with domain mapping, data retention, hot and cold cache and much more.

In this session you will discover 5 Kusto secrets which will help you to build powerful real time ingestions using and Eventhouse

Fabric: From real time data to actions

In this full day pre-conference the attendees will have a full view of the real time intelligence feature

Concepts of lambda architecture and how to implement them with EventStream

Implementing transformations in EventStream

Implementing policies in Kusto Database

Creating real time dashboards and its design features

Creating actions from your data using data activator

SQL Server Query Store Wonders

Query Store, a feature released in SQL Server 2016, never stops to provide new surprises to DBAs. It changed for the better the way we monitor and manage the performance of our SQL Server. Let’s discover what it is and how we can use it.

No-Code With Fabric: The challenges of Pipelines and Dataflows

Discover the several hidden tricks behind the usage of pipelines and dataflows in Microsoft Fabric and understand how they are very different than the similar object in Azure Data Factory

Secrets of Notebook Parallel Execution

Data Ingestion using notebooks can require many steps and the orchestration of these steps.

Besides the other options, the notebooks can orchestrate their own execution. The steps can be broken down in multiple notebooks and one main notebook can orchestrate the execution of the all of them.

Beyond this capability, the orchestration can trigger parallel execution. Instead of executing the steps in sequence, they can be executed in parallel.

The parallel execution of notebooks bring many secrets, such as the exchange of parameters, complex parameter types and management of dependencies.

In this session we will analyse the secrets of orchestrating parallel executions with notebooks.

Real Time Intelligence and KustoDB with Microsoft Fabric

In this session the attendee will discover why Fabric is very powerful for real time ingestion.

Using a tool capable to build an entire lambda architecture in a single environment, we will illustrate the different capabilities and also answer key questions.

Why is KustoDB better for real time? How to manage dimensions in a real time ingestion? How to discard the data from the speed layer while keeping the data from the batch layer?

All these details and much more will be included in this session.

Microsoft Fabric: Notebook Secrets No one told you Before

Use notebooks on the Microsoft Fabric environment is something new for many professionals, from data analysts to data integrators and many other professionals, this is completely new.

The notebooks have many secrets, together the pySpark development. For example:

Keep parameters between environments
Execute another notebook
Saving and reading JSON files
Using functions

and much more

In this session you will jump in development techniques using pyspark notebooks in Microsoft Fabric to make powerful data pipelines

Lakehouse Tables Optimizations and Maintenance

Discover secrets about detal tables optimization and maintenance

Microsoft Fabric: From Zero to Hero

Discover all the new feature of Microsoft Fabric and the incredible resources it has to offer to build a Data Ingelligence Platform.

Creating lakehouse, data warehouse, connecting them together, managing domains in a data mesh scenario, using real time data and much more, discover how easy it is to use Microsoft Fabric

Microsoft Fabric: Master your company data in a day

Discover in this single day training how to manage your company data in a single platform.

During the day we will dig deep on each one of the experiences in Microsoft Fabric: Data Engineering, Data Warehouse and Data Factory.

We will go deeper on ingesting data, making lakehouse maintenance, defining the data architecture, in special Data Mesh, defining a development lifecycle using source control and deployment pipelines and much more.

Data Mesh and Fabric: From the theory to the implementation

Microsoft Fabric comes to transform the way we work with enterprise Data Platform. At the same time, we also have an architectural shift: many companies are moving from a Single Source of Truth to a Data Mesh architecture.

In this talk you will discover how Microsoft Fabric supports the Data Mesh architecture, enabling powerful new approaches for the enterprise data architecture.

We will also desmistify data mesh, clearing all the gossips related to the architecture and focusing on the true reasons for its existance and implementation

Deployment Pipelines with Power BI and Fabric

Discover how to user Power BI Deployment Pipelines to control the development lifecycle not only for Power BI, but also for Microsoft Fabric.

You will discover not only the technical details about how they work, but also best practice about their implementation to control the development lifecycle.

Deployment Pipelines, GIT and Fabric objects life cycle

Using Deployment pipelines in Microsoft Fabric we can control objects lifecycle.

Not all objects are supported, but we already have control over notebooks, reports and semantic models.

Combining the power of Deployment Pipelines with GIT, we can create powerful development life cycles to support the process of entire microsoft fabric teams.
Either if they are fron-end teams building semantic models and reports or back-end teams building the enterprise data architecture, deplyoment pipelines and GIT can create powerful life cycle for the developers.

Fabric and the secure access to your data

Discover on this session the multiple options offered by Microsoft Fabric to safely access your data.

VNET Data Gateways, Private Endpoints and more, many options allow your data to be completely safe.

Fabric Real-Time and CDC: Ingesting in Real Time from Azure SQL

There is a mirroring solution from Azure SQL to Fabric and we also have the Data Pipelines and Dataflows for ingestion.

However, Real-Time Ingestion from Azure SQL using CDC is the best of the best: We get the data in real time and in incremental way, ensuring we will only be receiving changes to the actual data.

Transforming Data into Actions: Real Time Dashboards and Data Activator:

Data Activator is Fabric's secret to transform data into actions.

Many different methods can be used, such as triggers over EvenStream objects, triggers based on KQL queries in real-time dashboards, triggers in reports and more events.

Let's discover about Real Time Dashboards, triggers and actions creation and the many possibilities related to data activator.

Real Time Intelligence with Microsoft Fabric

In this session the attendee will discover why Fabric is very powerful for real time ingestion.

Using a tool capable to build an entire lambda architecture in a single environment, we will illustrate the different capabilities and also answer key questions.

Why is KustoDB better for real time? How to manage dimensions in a real time ingestion? How to discard the data from the speed layer while keeping the data from the batch layer?

All these details and much more will be included in this session.

ML Prompt Flow: The CoPilot's King

There are many different architectures being implemented to produce RAG, an LLM capable to analyse the private data for one company.

However, ML Prompt Flow is for sure above all of them, because it's capable to create an orchestration between different architectures resulting in a single co-pilot capable to get every piece of data your company has, on every format.

Git Source Control In Power BI and Fabric

Git support is a long awaited feature in Power BI. It's finally available in Power BI Desktop and Microsoft Fabric Portal (former Power BI Portal).

Let's discover how it works and what are the possible architectures we can implement using Power BI GIT Support

Data Mesh with Microsoft Fabric

In this session you will discover how Microsoft Fabric and the Power BI portal are a perfect environment for the creation of a Data Mesh architecture, bringing features for domain management and data sharing between domains as we haven't seeing before

Discover why and when you need Data Mesh, the differences between what's true and what's fashion and let's see a proposal for a practical data mesh implementation using Fabric

Data Factory - ETL in the Cloud

Data Factory is a powerful member of the Azure data platform. We can build powerful ETL solutions with no code and migrate existing on premise SSIS solutions to the cloud.

Come to this full day session to discover how it works and what you can build

Build your RAG solution with Microsoft Fabric and Azure

During this training day we will build a RAG solution using a bit more than the obvious components.

Instead of using only the LLM over the data, which provide unpredicted results, we will use a co-pilot with co-pilot studio and power automation, acquiring additional control over the result.

Instead of indexing relational data using cognitive services, we will build a query system where the LLM is capable to build a query for relational data.

Finally, to join the solution for structured and unstructured data in a single solution, we will using Azure Machine Learning Prompt Flow, illustrating a glimpse of the powerful capability to build a workflow over an LLM solution.

Let's break down what's fashion and what's reality and you will decide how to better use this architecture.

Azure SQL and Fabric in a click: Mirroring a Database

Discover how and why you should mirror an Azure SQL Database to Microsoft Fabric.

We will analyse it's limitations, features, and you will discover some surprises in relation about why and how you should make this mirroring.

Microsoft Fabric ETL for Azure Data Factory professionals

Usually everyone says Microsoft Fabric incorporates Data Factory ETL development resources. However, it's not so simple. Some concepts from Data Factory are not the same in Microsoft Fabric.

Let's discover the differences of the Pipelines and Dataflows in Fabric from the Azure Data Factory objects

Chat with your data using OpenAPI

AI is not the future anymore, it is the present. One challenging everyone is trying to solve is how to link your custom data with the powerful LLM models provided by OpenAI.

On this training day you will discover how to do that, either with modelled data and with unstructured data and documents.

We will talk about not only how to make the connection between OpenAI and your data, but also how to create UIs to access this environment, allowing the end user to talk with your data.

Chat with your data using OpenAI and Microsoft Fabric

On this session the attendees will discover how to use OpenAI to make queries to modelled data in lakehouses and data warehouses.

From using Semantic Kernel in notebooks to creating an architecture for business users, the attendee will discover a new way to provide data access in the company

Delta Lake Optimization and Maintenance in Microsoft Fabric

Delta Lake has many behaviours which needs to be taken in consideration when designing a data intelligence architecture.

On this session you will discover about these behaviours, the implications on the architectures and how to use notebooks for scheduled maintenance in lakehouses

Implementing Data Mesh with Microsoft Fabric

Microsoft Fabric comes to transform the way we work with enterprise Data Platform. At the same time, we also have an architectural shift: many companies are moving from a Single Source of Truth to a Data Mesh architecture.

In this talk you will discover how Microsoft Fabric supports the Data Mesh architecture, enabling powerful new approaches for the enterprise data architecture

Microsoft Fabric: Data Intelligence in a light speed

Discover what is the new Microsoft Fabric and how is it transforming our way to produce data intelligence architectures, from the PaaS of software such as Synapse and Databricks to a SaaS scenario, where we can build lakehouses and data warehouse focusing on the final result

OpenAI Intelligence with Web and Document scrapping

OpenAI had data updated up to 2021. How could you update this intelligence with more current data and also with private company data?

Microsoft provides a solution accelerator using all the power of OpenAI, Cognitive Services Search and Forms Recognizer to allow us to index custom company or web data - or even both - and build a custom LLM with the intelligence needed by our company

Power BI: Securing the connection with Virtual Network Data Gateways

Power BI needs to access Azure resources very often: Synapse Analytics, Azure SQL and much more. How can we ensure the connection will be secure and the communication between Power BI and Azure resources will not cross the public internet?

Using Virtual Network Data Gateways we can stablish a private connection between Power BI and Azure resources, avoiding any open doors to intruders.

SQL Server Query Store Wonders

Query Store, a feature released in SQL Server 2016, never stops to provide new surprises to DBAs. It changed for the better the way we monitor and manage the performance of our SQL Server.
Let’s discover how to use query store to solve many different query performance problems.

SQL Server 2022: Query Optimization Evolution

Query Optimization features are evolving on every SQL Server version. After query store in SQL Server 2016, many new features are built over Query Store foundations.

On this training day we will start learning about query store and the new features created from it on each SQL Server version and conclude with the new optimization features in SQL Server 2022 which are also built over the Query Store Foundations.

SQL Server 2022: Parameter Sensitive Plan Optimization

SQL Server 2022 brings a very interesting new feature to solve parameter sniffing: The query plan cache can hold multiple query plans for each query

Let's discover how this feature works

Talk with your Data Using OpenAI

We are on the era of AI and the first challenge we face is to use the existing resources together our own data.

Discover how to use OpenAI to make questions to your own company knowledge base

Self-Service ETL: The PowerBI Data Flows

What was already good has become even better. We were already able to make ETL in PowerBI using M, now, using the PoweBI Data Flows we are able to store the ETL on the PowerBI Portal, control the ETL execution, the data among many ETL steps and share the result with many Power BI files, instead of having to duplicate M scripts across different.PBIX files

In this session, you will understand better what Data Flows can bring to our already powerful self-service BI tools

Power BI Datamarts: How to use them

Power BI Datamarts was first announced almost an year ago and it's still cause lots os misunderstands. On this session we will explore where Datamarts fits on the data platform architecture and how this feature can improve your entire data environment.

Power BI: Datamarts and Aggregations for Better Performance

One additional benefits of the datamarts is how easy it is to build aggregations using the datamarts. No more users trying to negotiate with the IT team to build them on the DW, now you have your own places for the aggregations

Power BI Everywhere

Power BI can now be integrated with Microsoft Office, allowing us to use Power BI visuals everywhere: Word, PowerPoint, Outlook.

This allows us to maximize the power of storytelling

SQL Server 2022: Intelligent Query Processing new Features

SQL Server 2022 is using Query Store, released with SQL Server 2016, to improve even more the concept of Intelligent Query processing.

We have new features such as DOP Feedback, CE Feedback, Parameter sensitive plan optimization and more

SQL Server 2022 Improvements

Some of SQL Server 2022 new features requires an entire session for them, such as the Intelligent Query Processing, , SQL Ledger and Azure SQL Link.

But there are many more features in SQL Server 2022 that would be almost forgotten. WE will talk about them:

Concurrent GAM and SGAM
Auto Drop Statistics
XML compression
Dynamic Data Masking Granular Permissions

and more!

Using Azure Log Analytics to build SQL Baselines and more

Log Analytics is one of the basic Azure services which should be uses from day one when moving to the cloud.

It's a so interesting services that we can use even before going to the cloud and it makes way easier to generate a server baseline for our SQL Servers, a task all of us know it's important but we don't dedicate enough time for it

Synapse Dedicated SQL Pool: Whats hidden behind the scene

Many DBA's mistakenly think a Synapse SQL Dedicated Pool is just a regular SQL Server Database
This mistake can lead to below optimal configurations, bad performance and loose of investment.

In this session you will discover everything especial about SQL Dedicated Pool that makes it way more than a regular SQL Server Database and how to configure it for the maximum performance possible

Starting a Data Warehouse with Synapse SQL Link

Synapse SQL Link can integrate multiple SQL data sources inside a Synapse dedicated pool, allowing us to start a data warehouse from a single point

The support for Azure SQL, SQL Server 2022 and SQL Server 2022 on VM's provides us with a powerful tool to integrate multiple data sources

Solving big problems with Query Store Hints

Query Store Hints are a great innovation to solve big problems on our queries.

Let's discover how the query store hints can help us to easily solve the so usual parameter sniffing problem and how can we manage the application of query store hints on our environment

Real Time Data With Power BI and Azure

Ingestion Architecture is an essential piece to work with Architecture. Power BI have an important role on this, working together Azure to ingest data and provide real time visualizations.

Come to this session to see a lambda architecture being built using Event Hub, Stream Analytics, Power BI and Synapse

Azure SQL Configuration Secrets

Do you think you know Azure SQL?

You may be surprised with the number of secrets it has for you. Let's discover on this session how to make our Azure SQL better and more secure using these secrets on the best way possible.

Deployment Pipelines on Power BI

Developing a solution with Power BI is like any other development process: We need to follow stages for the approval of solution, from development to production.

Let's see how the Deployment Pipelines in Power BI help us with this

Power BI Goals: Managing KPI's with Power BI

Power BI Goals is a quite new and powerful feature to manage KPI's and incorporate KPI's in our analysis and Dashboards.

In this session you will discover how how Power BI Goals work and how they can be used to integrate KPI's in our analysis

Protecting your data with SQL Ledger

Azure SQL Ledger is a great innovation to protect SQL Server data and ensures the safety for solutions that, otherwise, would be looking into other technologies.

Let's discover how Ledger works and how it can make your architecture easier

Monitoring and Building Performance Baselines for Azure SQL with Log Analytics

Log Analytics is in the core of Azure Monitoring solution for all Azure features and this includes Azure SQL as well.

We can use Log Analytics to achieve a great monitoring solution and easily build performance baselines for our Azure SQL Databases using Azure Log Analytics

Power BI In an Enterprise Environment

Power BI is well-known as a self-service visualization tool, but many developers don't know how well power bi can fit in an enterprise environment.

Let's start from the basics and talk about power bi dataflows and other features that make power bi a great tool for an enterprise environment, working very well together data warehouse, big data and data lake house tools

Real Time Data with Power BI

There are many scenarios of data ingestion when we would like to see our real time data. Microsoft made a good work improving the features for real time data in power bi.

In this session we will talk about the existing features for real time data ingestion in Power BI, how they integrate with new applications or with existing real time data feeds

Saving Money with Azure Data Share

Azure Data Share appears to be a very simple solution, but it's easy to miss the potential this solution has to save money and work from development and dba teams.

Power BI: Using streaming for real-time data

There are many scenarios of data ingestion when we would like to see our real time data. Come to this session to learn how to build reports over real time data using power bi

Power BI: Using composite models and Aggregations for better performance

These two features, composite models and aggregations, are great when used together, they can result in a great performance improvement, as you will see on this demo-full session

Power BI and Synapse: Better Together

Let's talk what benefits we can get when using Power BI and Synapse together, using features such as the Synapse Performance Accelerator, besides other benefits for performance

Power BI: Incremental Refresh - Why load everything?

Incremental Refresh is a very important feature to ensure we can read from the data source only the new data, instead of load everything all the time. This will improve a lot the refresh performance of your data

Azure SQL Networking Secrets

Provisioning an Azure SQL DB is very easy, but are the default configurations good for you? How to ensure our database is really safe? Come to this session to discover this and a lot more

Synapse Spark Pool and Azure ML

The spark pool in Synapse allow us to build and manage a Data Lake and also supports Delta Lake features. After the integration with Azure ML was included, it became a very interesting option to manage our Data Lakes and extract intelligence from our data

Secrets of SQL Dedicated Pool

Many DBA's mistakenly think a Synapse SQL Dedicated Pool is just a regular SQL Server.
This mistake can have expensive consequences. I already saw people complained about the performance of the SQL Dedicated pool, without realizing the default are not tuned for performance and the tables requires special and careful configuration.
In this session you will discover everything especial about SQL Dedicated Pool that makes it way more than a regular SQL Server.

Data Lake House in the Cloud: The Azure Solutions

On this session you will discover what's a Data Lake House, why we need it and how can we implement this solution in Azure Cloud

Let's discover the power of having the data on the right hands at the right time with a Data Lake House

Developing for Flexible Data Sources

What happens if suddenly someone asks you to adapt that wonderful dashboard for another data source?

Maybe you developed it over excel and someone imported the data into the cloud, or the opposite, someone exported the data to excel to make the dashboard more independent.

Will your dashboard survive this challenge? Is your development ready for this challenge? Let's discover!

Optimizing Model and Dashboards using DAX Studio

Power BI is able to deal with huge amounts of data, but we need to take care with performance. Small differences in the way we build a dashboard can have huge impact on the solution performance.

Power BI has some tools to help us analyze the performance of the solution and when we need to go deeper, DAX Studio can point where the problem is

Data Factory and Power BI: Better Together

While Data Factory is the cloud ETL solution for Azure, Power BI has Power Query, a great self-service ETL tool based on M language.

In this session, you will discover why you still need Data Factory and Power BI together when you should use either Data Factory or Power Query and how to use both to build a great ETL solution.

Going to Azure SQL: Admin differences no one told you before

Many companies believe administering a SQL Server in the cloud is the same as administering a SQL Server on premises and that is a big mistake.

Optimizing, Monitoring, Security, there are many differences on the administration you will learn during this session, allowing you to become a better cloud DBA.

SQL Server in the Cloud: The available options

Among PaaS and IaaS, we have many SQL Server Cloud options: Azure SQL Db, SQL Server Managed Instance and Virtual Machines. How should we choose between them? How should we manage them? Let's discover the best approaches to use SQL Server on the cloud

Power BI dataflows : Going beyond the self-service

What was already good has became even better. We were already able to make ETL in Power BI using M, now, using the Power BI Data Flows we are able to store the ETL on the Power BI Portal, control the ETL execution, the data among many ETL steps and share the result with many Power BI files, instead of having to duplicate M scripts across different .PBIX files

This feature allow us to create a more complex and re-usable ETL solution, creating small dataflows and allowing them to be reused in a dataflow hierarchy, which can be re-used in many different datasets, creating a more robust and reusable architecture.

In this session you will understand better how the dataflows are an important feature in making Power BI go way beyond the self-service

Cloud Security: Secure Access from your Applications to Azure SQL

Security is essential for our cloud applications, but the security in the cloud has many more challenges than on premises. Using Key Vault for passwords and keys, managed identity for cloud services, integrated authentication for Azure SQL and more.
In this session you will discover the differences between authentication on premises and in the cloud and you will learn how to go way beyond the basic user/password for authentication

Advanced ETL with Power Query

The Power Query M language provides a very powerful way to develop ETL, way beyond the basic features you know.

We can create functions, reuse functions, create loops, everything to make our data transformations easier. During this session you will see many advanced techniques using M and Power Query

SQL Graph Databases: Beyond Relational

Since SQL Server 2017 the Graph Database feature was released, enabling us to mix the relational model with a Graph model. Some existing flaws in this release are being fixed in SQL Server 2019, bringing graph objects closer to SQL Serve reality.

In this session, you will discover how Graph Databases in SQL Server works and what's new for them in SQL Server 2019

Why Synapse Analytics is beyond Azure SQL Datawarehouse

Synapse Analytics is being announced as the new name of Azure SQL Datawarehouse, but it's not only that. There is a broader view in Synapse analytics, beyond Azure SQL Datawarehouse. Let's see in this session what was improved, what changed, and how synapse analytics fits in the data platform.

Data Virtualization and Data Lake using SQL Big Data Cluster

Cloud and globalization bring to us more and more challenges every day. With the increase in the volume of data, we are evolving from data warehouses to big data and data lake architectures. In this session, we will better understand the concept of data virtualization, data lake architectures and how SQL Server 2019 Big Data Cluster can help us with these challenges

PowerBI: From Zero to Hero

Starting from the very beginning, in this session, we will cross together the main features Power BI has to offer, starting with the powerful self-service ETL using M language when needed, modeling our tables and finally using powerful features to create a great data visualization, allowing you to better understand all the power and flexibility of this tool and how it will fit into your solution

Activator and Lakehouse files: Using lakehouse as a drop zone

Activator is the event triggering system inside Microsoft Fabric. It's related to Real-Time Intelligence to trigger actions based on the data.

However, Activator can also trigger events related to Fabric objects and one of the best features is triggering events based on file events in lakehouses.

This feature makes it easier to use the lakehouse as a drop zone in a medallion architecture. Instead of requiring a drop zone in azure before ingesting into the lakehouse, the lakehouse itself can be the drop zone.

Combining features such as triggers over lakehouse files, SAS authentication in OneLake and more, we can build the drop zone of a medallion architecture inside a Fabric lakehouse.

In this way, we can bring the entire medallion architecture to inside Fabric

Dennes Torres

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