<p>
    <a href="https://datadrivencommunity.com/FutureDataDriven2024.html" target="_blank">Future Data Driven Summit 2024</a>&nbsp;is one of the largest FREE IT Conferences in the Data Community focusing on Data Platform, AI, Business Intelligence, Business Applications, Power Platform, and Analytics. IT Decision Makers, Developers, Business Analysts, IT Professionals, BI Managers, BI Professionals, Database Administrators, Architects, Digital &amp; Big Data business leaders.
</p>
<p>
    <b>Why Future Data Driven:</b>&nbsp;It is a great opportunity to learn from SQL Server Experts, Microsoft MVPs, SQL Server Authors, and Professionals who have been through and can relate to the obstacles you have day to day. This one day training event is unlike any other providing free training and networking.
</p>
<p>
    Don’t miss out on the benefits of early registration. Secure your spot on our special list and stay updated with all the latest news via email.&nbsp;<a href="https://datadrivencommunity.com/fdd2024registration.html">https://datadrivencommunity.com/fdd2024registration.html</a>
</p>
<p>
    <b>When:</b>&nbsp;September 25 2024,
</p>
<p>
    <b>Cost:&nbsp;</b>FREE
</p>
<p>
    <b>Where:&nbsp;</b>Live Stream Event
</p>
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    <iframe style="margin-top: 0px;" src="https://www.youtube.com/embed/9TfljONRDyU"></iframe><iframe style="margin-top: 0px;" src="https://www.youtube.com/embed/55lO6uz6CGo"></iframe><iframe style="margin-top: 0px;" src="https://www.youtube.com/embed/atARkGZjI14"></iframe>
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Future Data Driven Summit 2024

Many companies are looking to reduce licensing costs by moving databases currently written for SQL Server to PostgreSQL.  What are the technical pitfalls you might encounter when moving not only the database schema but re-factoring the code?

In this presentation, we will show how recreating the existing database schema in PostgreSQL is quite easy and migrating the data from MS SQL Server to PostgreSQL is not hard.  However, the old saying of “garbage in equals garbage out” applies to both views and stored code.

These two types of pre-compiled code will challenge the developer to use their skills to decipher code written years ago and convert it to modern day code that is performant, uses intermediate steps for debugging and uses logging for run time metrics.

In short, this presentation is a must for database developers that are tasked with moving and modernizing older SQL Server databases to PostgreSQL!


“Moving on up, to the Azure cloud, with a PostgreSQL database in the sky.”

As we stand at the cusp of an AI renaissance, it's clear that AI-enabled applications are becoming the new norm - from personal devices to the scale of cloud. Yet, it's not the technology alone that sets you apart — it's how you leverage it with your unique datasets. Your data is the key to delivering personalized experiences that your competitors simply cannot replicate. In this era, AI is the great equalizer, but your data is the ultimate differentiator.

The fusion of Generative AI with Azure SQL Database is not just about building applications; it's about creating experiences that are as unique as your data. It's time to step into the future and redefine what's possible with AI, and your business data.

AI, the Equalizer; Data, the Differentiator

One of the challenges facing enterprise architecture is maintaining consistency across the enterprise. This is challenging because data comes from disparate sources and systems representing different purposes, focuses, and objectives. On top of that, there needs to be more clarity regarding the terms Data Lake, Data Warehouse, Operational Data Store, etc. To overcome these challenges at an enterprise level, a framework is necessary to apply data uniformly, consistently, and meaningfully. The framework will transform data from multiple, disparate data sources into an operational data store, a consistent, homogeneous environment, as the single dependable source of truth for all reporting and analytics.

This session will present that framework and discuss the following:
	• The challenges facing data analytics and reporting due to multiple disparate data sources
	• An introduction to the various terms used today for data storage
	• The purpose of an Operational Data Store and how it improves data usability
	• The data zones for Raw, Refined, and Published data
	• How data standards simplify and enhance data usability
	• A method to provide primary and foreign keys across disparate data

An Enterprise Data Architecture Framework

Discover the power of Azure AI Studio for full lifecycle development of generative AI applications using your own data. From prompt engineering to deploying copilots, Azure AI Studio offers a unified platform equipped with pre-built Azure AI skills, multi-modal application development, and advanced evaluation tools.

Why You Should Attend:

Discover how Azure AI Studio provides a centralized environment for prompt engineering, enabling seamless integration of language, vision, and speech capabilities into your AI applications.

Deploy an Azure OpenAI Model and Chat with Your Data: Explore the deployment of an Azure OpenAI model and engage in insightful conversations with your company's data, laying the groundwork for copilot training.

Custom Orchestration with Prompt Flow: Gain insights into the custom orchestration capabilities of Azure AI Studio's prompt flow tool, empowering you to guide the behavior of your applications to better fit your specific use case requirements.

Deploy the Flow for Consumption: Take the final leap by deploying your well crafted copilot flow for consumption, empowering your company with an AI-driven assistant ready to enhance customer interactions and streamline operations.


Azure AI Studio: Orchestration, Prompt Flow and Copilots

Are you ready for a game-changer in database monitoring? Look no further! The Database Watcher is here, and it’s creating waves in the tech world. 

What’s the Buzz About? The Database Watcher is more than just a monitoring tool; it’s a revolution. Imagine having real-time visibility into your PaaS databases, with metrics, alerts, and performance insights at your fingertips. Whether you’re a developer, DBA, or data enthusiast, this tool will elevate your database management experience.

In this session, we are going to show you how easy it is to use Database Watcher, a cloud-native and cross-platform solution that supports multiple database types, versions, and platforms, to set up proactive database monitoring and collect very deep, unprecedented, rich data. You will see how Database Watcher provides easy to understand and read visualizations, with near real time monitoring, that can help you optimize your database performance, availability, and health. We will also focus on some of the exciting features coming soon, such as:
A unified dashboard that provides a comprehensive and customizable view of your database environment.
A smart alerting system that provides actionable recommendations based on best practices and industry standards.
A powerful query analyzer that tracks, captures, and analyzes queries executed on your databases, and provides insights into query performance, optimization, and tuning.
Join us in this session and discover how Database Watcher can help you take your DBA career to the next level by enabling you to proactively monitor, troubleshoot, and improve your database operations.

Be a Better DBA with Database Watcher: A Proactive Approach to Database Monitoring

Power BI is an interactive data analytics platform that allows users to create reports, dashboards, and visualizations from a variety of data sources. It is widely used for data visualization and exploration, as well as business decision-making. Semantic links are a key component of Power BI. They enable business analysts and data scientists to collaborate seamlessly and reduce data inconsistencies. Semantic links provide connectivity to semantic models via the Pandas ecosystem (Python) and the Spark native connector that supports PySpark, Spark SQL, R, and Scala. 
This session will demonstrate how semantic links facilitate collaboration between business analysts and data scientists, while ensuring data consistency across semantic models


Bridge the gap between Power BI and Data Engineering with Semantic links

Join us for an exciting session on building AI-driven applications using Azure Cosmos DB for MongoDB. Discover Cosmos's new document database, designed to support the development of modern AI applications. vCore-based Azure Cosmos DB for MongoDB provides a fully managed, MongoDB-compatible database service, allowing developers to leverage native Azure integrations and benefit from a low total cost of ownership (TCO). Whether you're migrating existing applications or creating new ones, enjoy the familiarity of the vCore architecture and build powerful AI solutions with a single database solution.

Build AI-powered apps with Azure Cosmos DB for MongoDB

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

Chat with your data using OpenAI and Microsoft Fabric

Imagine having a conversation where you can not only talk but also show and interact with images and videos!  

This session unveils the magic of combining Azure OpenAI's revolutionary GPT-4 Turbo with Azure AI Vision. Witness how this fusion unlocks a new era of chat and image interactions, empowering you to seamlessly integrate text and visuals

Chat with your Videos using Azure Open AI and Vision 

Copilot for Power BI is an exciting feature that leverages generative AI to enhance your data analysis and visualization experience, Copilot allows you to create reports automatically by selecting a topic or prompting Copilot on a specific subject.
Copilot is accessible in both Power BI Desktop and the Power BI service.

Copilot In Power BI

Embark on an adventure through the vital ingredients and methods required to construct visually striking and highly effective reports, guaranteeing a flawless experience for your audience.

The standard report experience tends to overwhelm users with excessive buttons, icons, pop-ups, and interactions. Many of these default features can be perplexing, dysfunctional, or even result in data misrepresentation. To streamline the user experience, thoughtful attention must be devoted to aspects such as visual header icons, filters and slicers, visual interaction types, and page or drillthrough navigation.

Moreover, selecting the perfect visual to narrate a story, determining which report elements (titles, labels, chart elements) best elucidate the data, and designing report page layouts that follow a logical progression, all involve a blend of science, art, and a touch of enchantment.

Upon completing this presentation, you'll possess a winning formula for converting your Power BI reports into tailored masterpieces that appeal to your audience's unique tastes and inclinations. Ignite your inner data connoisseur and elevate your reporting prowess to unparalleled levels with this mouthwatering session on Power BI report design and user experience.

Curating the User Experience: A Chef's K.I.S.S to Reporting

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they’re also surrounded by a lot of hyperbole and confusion. In this presentation, I will give you a guided tour of each architecture to help you understand its pros and cons. I will also examine common data architecture concepts, including data warehouses and data lakes. You’ll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality. Best of all, you’ll be able to determine the most appropriate data architecture for your needs.

In addition, I will delve into how Microsoft Fabric can be leveraged within each of these architectures. From integrating data fabrics for enhanced data management and governance, to implementing data lakehouses for streamlined analytics, and establishing data meshes for decentralized data ownership, Microsoft Fabric offers robust solutions and tools. By the end of this presentation, you’ll have a clear understanding of how to use Microsoft Fabric to build and optimize these diverse data architectures, tailored to meet your specific requirements.

Data Architectures and Microsoft Fabric

Data Exploration and Analysis using Amazon Q

Join us in this session to explore how Real-Time Intelligence streamlines data ingestion, enables real-time querying, and enhances overall analytics capabilities. Learn practical techniques to leverage this powerful platform in your data-driven workflows. Real-Time Intelligence in Microsoft Fabric empowers organizations to scale up their analytics solutions while democratizing data access. It’s essential in various enterprise scenarios, including cybersecurity, asset tracking, predictive maintenance, supply chain optimization, and more. In this session, we’ll delve into the critical aspects of data ingestion within Real-Time Intelligence. We will cover what's new in Kusto, Copilot in Eventhouse and the new Real-Time Dashboard in Kusto.

Data Ingestion in Microsoft Fabric Real Time Intelligence

Event-driven paradigm has been more and more popular thanks to serverless solution able to catch process and scale according to the event.

But when you have a serverless data warehouse, such as BigQuery on Google Cloud, how can you leverage event driven design to synchronize different sources in data mesh architecture? You have several solution and even an open source solution that I create and released this year!

Data platforms synchronisation journey at Carrefour

In this talk, we delve into the fascinating world of conversational AI, exploring the transformative power of Microsoft Azure OpenAI in reshaping communication dynamics. This session throw an insight through the journey from raw data to meaningful dialogue, showcasing how Azure OpenAI seamlessly integrates with your data ecosystem to deliver personalized interactions. We will discuss the underlying technologies driving this revolution, from natural language processing to Large Language Models. Moreover, I will highlight real-world applications and success stories, illustrating how organizations across various industries are leveraging Azure OpenAI to enhance customer engagement, streamline support services, and drive business growth. Whether you are a seasoned AI enthusiast or new to the field, this talk offers valuable insights into the potential of Microsoft Azure OpenAI to redefine the way we interact and communicate in the digital age. Join me as we explore the frontier of conversational AI and discover the endless possibilities it holds for transforming communication as we know it.

Data to Dialogue: How Microsoft Azure OpenAI Transforms Communication with Personalized Interactions

Terraform lowers the bar to entry for adopting Infrastructure as Code. However, to really get the most out of it we need to look at more advanced patterns and practices to really operationalise it and realise the value when managing infrastructure at scale.

In this session we will look at how to leverage some of the more advanced features of terraform by building our own reusable  modules. As well as built in functions and techniques to ensure a maintainable codebase. Finally, looking at how to automate deployments as part of a wider platform deployment including databases and application code in Microsoft Azure.

Defining and Deploying a Data Platform with Terraform.

This session will share an overview of the SQL Tools offered by Microsoft. It focusses on SQL development using SQL Server Data Tools in Visual Studio, talks about the convenience of using SQL projects. It digs into how SQL Projects can offer different deployment options and integrate into the CI/CD pipeline. It talks about how to use GitHub Copilot for SQL and shares intelligent insights on how AI can aid regular database development. 

Developer tools for SQL in Visual Studio- from AI to DevOps

Enabling your database for AI applications has never been easier. With the native vector support in Azure Cosmos DB, you can effortlessly deliver rich AI experiences to your users. We’ll start by covering the basics of vector embeddings, vector indexing, vector search, and Retrieval Augmented Generation (RAG). These fundamentals set the stage for live demos showing how you can leverage the power of AI right inside your transactional database. You’ll see how Azure Cosmos DB can store and index vector embeddings from Azure OpenAI right alongside your transactional data, run blazing fast vector searches, and deliver natural language responses to natural language questions about your data. Attend this session to accelerate your AI readiness for the future!

Empowering AI Applications with Vector Search in Azure Cosmos DB

The new Eventhouse from Microsoft Fabric consolidates over 100.000 real-time analytics databases in one logical group.

But why do that, and what cause do I have to use it?

Attend this sessions and find out why the Eventhouse should be your go to approach when working with real-time analytics.

We start of by loading data from Eventstream to a KQL database and then go from there to show you the fastest possible way in Fabric to load data through the medallion architecture using the Eventhouse and the features from the KQL databases.

We end in PowerBI to see the results.

I promise this session will be fast paced, contain a lot of information and still be fun and interesting.

Eventhouse - medallion architecture in sub seconds

In the realm of data analytics, the representation and analysis of interconnected data play a pivotal role in uncovering hidden patterns, relationships, and insights. This talk delves into the intriguing domain of graph semantics within Fabric Real Time Analytics, a powerful and scalable analytics service offered by Microsoft.

The session aims to provide a comprehensive understanding of how graph data structures can be leveraged within Fabric Real Time Analytics to navigate complex relationships inherent in diverse datasets. Attendees will gain insights into the theoretical foundations of graph semantics and its practical applications in real-world scenarios, with a particular focus on optimizing data exploration and analysis.

The discussion will cover key topics such as graph database fundamentals, query languages for graph data, and the integration of graph semantics into the Fabric Real Time Analytics ecosystem. Practical examples and use cases will be presented to illustrate how graph-based approaches can enhance data exploration, uncover hidden dependencies, and facilitate advanced analytics.

Furthermore, the talk will address the seamless integration of graph semantics with other Azure services, showcasing the potential for cross-functional insights and the ability to derive holistic perspectives from interconnected data sources. Attendees can expect to leave with a deeper understanding of how graph semantics can be harnessed to unlock the full potential of their data within the Fabric Real Time Analytics environment.

Whether you are a data scientist, analyst, or a technology enthusiast, this talk offers a valuable opportunity to explore the intersection of graph semantics and Fabric Real Time Analytics, paving the way for more informed decision-making and transformative data discoveries.

Exploring Graph Semantics in Fabric Real Time Analytics: Unveiling Insights through Connected Data

Join me for an enlightening session as we delve into the revolutionary world of Microsoft Copilot Studio. In this session, we will explore the cutting-edge capabilities of Copilot Studio, Microsoft's groundbreaking AI-powered coding assistant, designed to enhance developer productivity and streamline the software development process.

During this session, you'll discover how Microsoft Copilot Studio leverages state-of-the-art machine learning models and advanced natural language processing techniques to understand code context, provide intelligent code suggestions, and assist developers in writing high-quality code faster than ever before. We will delve into its features, including code completion, documentation generation, error handling, and code refactoring, demonstrating how Copilot Studio can accelerate your development workflow and boost code quality.

Furthermore, we will discuss best practices for integrating Microsoft Copilot Studio into your existing development environment, whether you're working on web applications, mobile apps, or cloud-based solutions. You'll learn how Copilot Studio seamlessly integrates with popular code editors and IDEs, empowering developers to leverage its capabilities within familiar workflows.

Whether you're a seasoned developer curious about the latest advancements in AI-driven coding assistance or a tech enthusiast eager to explore the future of software development, this session offers valuable insights and practical guidance on harnessing the power of Microsoft Copilot Studio to unlock new levels of productivity and innovation in your coding journey. Join me and embark on a transformative exploration of the future of coding with Microsoft Copilot Studio.

Exploring the Future of Coding: Introducing Microsoft Copilot Studio

Security is a top priority for Microsoft Fabric. As a Fabric customer, you need to safeguard your assets from threats and follow your organization's security policies. The Microsoft Fabric security session serves as an end-to-end security overview for Fabric. It covers details on how Microsoft secures your data by default as a software as a service (SaaS) service, and how you can secure, manage, and govern your data when using Fabric. This will be an engaging session and you will get an opportunity to provide direct feedbacks to the product team. If you are a data analytics professional implementing Fabric or if you are an IT/InfoSec admin who wants to make sure that proper controls are in place for your organizational tools, then don't miss this session.

Fabric Security - Everything you need to know

Microsoft Fabric offers a centralized data hub, where all your data is stored in OneLake. Parquet as the open format of OneLake allows you to connect many different workloads within Fabric, including Power BI, to your data. Beside Import mode and DirectQuery mode, OneLake allows you to facility DirectLake mode in your semantic model, which combines a performance close to Import mode with the convenience of directly accessing the data from OneLake (without the need for a regular refresh). In order to bring your data into shape, Fabric comes with several ways of transforming your data (e. g. Dataflow or PySpark notebooks). Join this session to learn from best practices in making the content of OneLake available in your semantic model.

FABRICate your Data Model

Explore the cutting-edge capabilities of Microsoft Fabric as we delve into the world of predictive modeling for healthcare. This session, "From Data to Diagnosis," offers a comprehensive guide to using Microsoft Fabric for developing machine learning models that predict diabetes progression. The presentation will cover essential techniques and best practices in data handling, model training, and deployment within the Microsoft Fabric ecosystem.

From Data to Diagnosis: Building Predictive Models with Microsoft Fabric

After loading all of your data into OneLake, you might be wondering how to ensure its quality. In this session, we’ll explore and demonstrate how Soda can help you monitor data quality with automated data testing and alerting, write and monitor data contracts, and browse data quality metrics in a user-friendly dashboard.

From Flat to Sparkling: Monitoring Data Quality with Soda in Microsoft Fabric

In this session, we will embark on an exciting journey to explore the capabilities of Data Factory in Microsoft Fabric. This session is designed for beginners who are eager to learn how to leverage Data Factory to streamline their data integration tasks.

We will start by introducing Data Factory and its role in the Microsoft Fabric. We will then delve into the core components of Data Factory, including pipelines, data flows, and many more.

Participants will learn how to create and manage data pipelines, which are key to automating data movement and transformation. We will also cover how to use data flows to transform data at scale.

By the end of this session, participants will have a solid foundation in using Data Factory in Microsoft Fabric, empowering them to harness the power of their data more effectively.

Join us for this informative session and take the first step towards mastering Data Factory in Microsoft Fabric!

Getting started with Data Factory in Microsoft Fabric

Are you wondering how you as SQL expert fit in the modern world of AI? In this session we will show you how to build generative AI applications with SQL including RAG patterns, hybrid search, and "chatting" with your data. You will also learn and see demonstrations of Microsoft Copilot experiences to help you become more efficient with your SQL estate. 

Getting started with SQL and AI

Give your data the voice through:
•	T ell the story
•	H igher understranding
•	Engaging audiance

•	V isualization (explore / explain)
•	O rganize (so end-user can understand)
•	I mprove (and extract business value)
•	C lean (so you can trust)
•	E xtract-Transform-Load (unlock analytical potential)

It's not just visualization front and extracting business value from the data but also what is behind the scene to make this happened through several practical PowerBI examples.


Give your data the voice + what is behind the scene - with practical PowerBI examples

Tired of complex and costly cloud data platforms or don't know where to begin?

Join us for an enlightening session where we guide you step-by-step to build a robust yet budget-friendly Azure data platform. Led by a seasoned Azure expert, we reveal insider tips to maximize Bang-for-Buck through optimal architecture, savvy data stores, and intelligent scaling.

Expect practical takeaways - arming you with an easy blueprint, clever tool-kit and pro techniques to build a data platform that doesn't blow the budget.

Whether starting small or boosting bigger platforms, this session shares hard-won lessons so you craft an Azure data platform with commanding power at disruptively low total cost. Depart with more budget for innovation and a competitive edge for your business.

Guide to Building a Powerful Yet Cost-Effective Azure Data Platform

In today's rapidly evolving digital landscape, security teams and application owners are the unsung heroes safeguarding organizations' most valuable assets.

This session is designed to arm attendees with the advanced tools and methodologies needed to strengthen organization's data defenses. In keeping with the dynamic nature of data security, the session content includes the latest features and advancements in the field to the day of the event.

Led by Veronica Marinov, Security Researcher at Sentra, and David Trigano, Director of Product Management at Sentra, we delve into the intricacies of modern data security challenges and unpack state-of-the-art solutions. We will cover advanced best-practices, real-time threat detection, secure data architecture, and compliance strategies that align with global data protection standards. Through practical demonstrations, interactive discussions, and sharing cutting-edge research, this session aims to elevate attendees’ expertise in securing multi-cloud datastores against sophisticated cyber threats and ensuring data integrity and confidentiality.

How to effectively secure and protect sensitive data

Income Statement Co-Pilot is a Generative AI based solution using Microsoft Co-Pilot Studio. The session would be a demo of the solution . the solution architecture. best practices and lessons learned. 

Income statement Co-Pilot

Organizations globally seek to mature their analytical strategy in order to be more competitive. Recent innovations in technology have opened a wider door to advanced capabilities like predictive or prescriptive analytics. Without data scientists or some form of AI engineer available, this leaves many data engineers being tasked with creating their organization's advanced analytics…without any prior experience.

If this is you, or if you are looking to make a career shift into advanced analytics and don't know where to start, this kick-starter session is for you. We'll clearly define what we mean by advanced analytics, highlighting the differences between that and the analytical solutions you see more regularly in the market. With the end goal defined, we will highlight necessary skillsets that the average data engineer might need, and learning resources to help engineers get started.


Intro to Machine Learning as a Data Engineer

In this session we will have an introductory overtime of Microsoft Fabric. Understanding the components and seeing examples of the different personas approach. Will review the approach for Data Factory, Data Engineering, Data Science, Data Warehousing, Real-Time Analytics and Power BI. Finally, we will see how to start getting involved with the new platform for Data analytics for the era of AI

Introduction to Microsoft Fabric - Data analytics for the era of AI

More and more organisations are building data platforms in the cloud, often utilising Spark and tools like Databricks to build data engineering pipelines. These distributed computing tools can can be incredibly powerful, processing huge datasets incredibly quickly, but can have a steep learning curve. Often teams migrating older on-premises data warehouses to cloud solutions like the Lakehouse rightly concentrate on getting good data over getting the best performance. But are you getting the most out of these shiny new tools?

In the cloud, data pipeline performance can have a big impact on monthly cost, making it much easier to to justify spending time getting things running faster and more efficiently. This talk aims to show you the common pain points when working with Spark using Databricks, showing you where to look, what to look for, and what can be done to improve things.

We’ll look at how the architecture of Spark is reflected in the Spark UI, and how to use the UI, along with query plans and the cluster metrics to get a good understanding of if whether you’re wringing all the performance you can out of your cluster, or burning cash on excess compute. We’ll cover a list of quick wins to improve performance, and then look at how to identify some common problems that hurt performance. 

By the end of the session, you’ll know how to check if your data pipelines are running well, and if the clusters you have are fit for the job. You’ll hopefully have a few quick ways to improve performance, save some money or both! You’ll also know how monitor performance after making changes, so you can check if they made a difference, and if they did earn some kudos with your team.

Introduction to Performance Tuning on Azure Databricks

In today's rapidly evolving business landscape, organizations are increasingly turning to data analytics to drive process improvement and optimization. By leveraging the power of data analytics, organizations can identify inefficiencies, streamline operations, and drive continuous improvement across various industries and business functions.

Once inefficiencies are identified, data analytics can be used to streamline operations and improve process efficiency. This can involve optimizing resource allocation, reengineering workflows, or automating repetitive tasks. Furthermore, data analytics enables organizations to drive continuous improvement by providing real-time insights into process performance. This continuous feedback loop allows organizations to iteratively improve processes over time, leading to significant gains in efficiency and effectiveness.

However, implementing data analytics for process improvement and optimization is not without its challenges. Organizations must ensure that the data used for analytics is accurate, complete, and up to date, as poor data quality or lack of data availability can lead to unreliable insights and suboptimal decision-making. Additionally, integrating data analytics into existing systems and processes can be challenging, requiring strong alignment between business goals, analytics initiatives, and operational processes.

Despite these challenges, the benefits of leveraging data analytics for process improvement and optimization are substantial. By bridging the gap between data analytics and process improvement, organizations can stay ahead of the competition, drive operational excellence, and deliver superior value to customers.

Leveraging Data Analytics for Process Improvement & Optimization

Are you ready to take your Power BI reports to the next level and make them truly shine? Join us for an immersive and dynamic session where we'll unveil a plethora of captivating features designed to elevate your reports to new heights of brilliance! Through a series of captivating demonstrations, we'll showcase a rich tapestry of techniques guaranteed to make your Power BI reports stand out amidst the sea of data.

Prepare to embark on a journey through the enchanting world of Power BI customization as we unveil a treasure trove of features and functionalities that will transform your reports into captivating works of art. From dazzling visualizations to interactive elements, we'll explore some little known features that will make your Power BI reports pop.

Don't miss this opportunity to unlock the full potential of Power BI and unleash your creativity like never before. Join us and discover how to infuse your reports with a touch of magic that will leave your audience spellbound and craving for more!

Make Your Power BI Reports Pop

In this session, we'll explore the recently announced JSON features and other T-SQL enhancements in Azure SQL Database. JSON native type was one of the top requested features & we will explore you can leverage the native JSON data type for faster query processing, modifications, and searching.

The newly added ANSI SQL JSON aggregates - JSON_OBJECTAGG & JSON_ARRAYAGG allow you to aggregate existing relational data into any complex JSON document easily. Discover the advantages of leveraging JSON features in T-SQL to modernize your cloud native applications and interact with other services seamlessly.

We will also look at the most requested T-SQL REGEX functions available for preview in Azure SQL DB. The newly added REGEX functions enable you to write concise queries that perform complex data pattern matching, extraction, validation and manipulation. We will explore how to enable hybrid search capabilities using LIKE predicate, Full-Text, RegEx & Vector functions.

Modern T-SQL using JSON & RegEx for cloud native developer

Artificial intelligence (AI) significantly enhances the efficiency of data cataloging and documentation, automating many tasks in managing data products and datasets. However, the integration of AI introduces various risks:

Bias in Metadata Generation: AI-driven systems may generate biased or skewed metadata if the underlying models are trained on biased data or flawed algorithms. This can affect data tagging, categorization, and decision-making processes.

Quality and Accuracy Issues: AI may fail to capture or describe complex data nuances accurately, leading to data tagging or metadata errors. Such inaccuracies can be especially problematic in critical sectors like healthcare or finance.

Lack of Contextual Understanding: AI might not fully grasp the broader context of the data, missing regional variations or changes in data relevance, which could make the data seem outdated or irrelevant.

Security and Privacy Concerns: AI in data cataloging might inadvertently reveal sensitive information or create security vulnerabilities, exposing more data than intended.

Dependency and Overreliance: Heavy reliance on AI for data management could make organizations less inclined to utilize human expertise, which is vital for handling complex or sensitive data.

Scalability and Performance Issues: Although AI can efficiently process large data volumes, scalability challenges may arise as data grows in volume and complexity, potentially slowing down data processing and cataloging.

Ethical and Legal Compliance: AI must adhere to ethical standards and legal regulations like GDPR or HIPAA. Non-compliance due to AI inaccuracies can result in serious legal and financial consequences.

A combination of technical measures, robust governance frameworks, and human oversight is essential to address these risks. 

Strategies include:
Implementing bias mitigation techniques.
Enhancing data quality assurance.
Strengthening contextual understanding.
Prioritizing security and privacy.
Reducing dependency on AI.
Optimizing scalability.
Ensuring ethical and legal compliance.
Fostering continuous education and robust AI governance.

These measures help ensure that AI in data management is efficient and trustworthy.

Navigating the Minefield: Mitigating Risks of AI in Data Cataloging and Documentation

Delta Tables provide amazing out of the box features such as ACID Compliance and Time Travel to your datalake but there's a world of optimization beyond the default settings. 

This session is designed to take your Delta Lake to the next level. We'll start with a brief introduction to Delta Tables and quickly dive into advanced optimization techniques. 

We'll explore topics such as Delta Transaction Log retention, deletion vectors, liquid clustering, change data capture, and generated columns. 
 
Join me to unlock the full potential of your Delta Lake and make informed decisions that go beyond just leaving things at their defaults.


Optimizing Your Delta Lake: Beyond the Defaults

Delta has become a widely used tool by data professionals to build effective and reliable Lakehouse’s.  Yet, questions arise regarding its performance with large datasets, its ability to handle skewed data, and its concurrent write management. In this session, will dive deep into optimization options and methods that will improve your Lakehouse performance.

Delta files are not ordinary data files but are key in making Lakehouse efficient, optimal, and scalable.  However, optimizing delta files and tables in Databricks can be challenging and even a daunting task.  Techniques like partitioning and z-ordering can be limited, inflexible, and challenging to implement, especially when your data is constantly changing or growing.
This session will introduce you to the new liquid clustering technique, a cutting-edge approach that is more flexible and adaptable to data layout changes. This will not only enhance your query performance but also simplify your optimization process. 
Furthermore, we will explore various other Delta file optimization techniques, such as data skipping, z-ordering, and vacuuming in Databricks. These techniques will help you maximize the value of your Delta files while minimizing resource utilization and costs.
By the end of this session, you'll have the necessary knowledge and tools to optimize Delta files and tables for your own Lakehouse. 

Quest to Delta Optimisation

Real-Time Intelligence empowers your organization with data insights and visualization, offering a comprehensive solution for event-driven scenarios, streaming data, and logs. All organizational data converges in the Real-Time Hub, connecting various data sources seamlessly. With no-code connectors, it provides immediate visual insights, geospatial analysis, and trigger-based reactions. Once connected, the SaaS solution offers full accessibility, managing data ingestion, transformation, storage, analytics, visualization, tracking, AI, and real-time actions. Your data remains protected, governed, and integrated across the organization, aligning with Fabric offerings. Real-Time Intelligence transforms data into a dynamic, actionable resource, driving value across the organization.

Real-Time Intelligence in a click

Streaming data facilitates the constant flow of diverse and up-to-date information, enhancing the models’ ability to adapt and generate more accurate, contextually relevant outputs. This dynamic integration of streaming data enables generative AI applications to respond promptly to changing conditions, improving their adaptability and overall performance in various tasks.

Real-time streaming with Azure HDInsight on AKS for Generative AI

In the rapidly evolving world of data management, migrating from traditional SQL databases to modern data platforms like Microsoft Fabric is crucial for leveraging advanced analytics and real-time capabilities. This tech session focuses on effective strategies for migrating data from SQL to Fabric, providing practical insights and how to obtain the best performance,

This interactive, demo-based session will equip attendees with the knowledge to:

Migrating SQL Data to Fabric:
Understand the process and best practices for migrating data from SQL databases to Microsoft Fabric, ensuring data integrity and minimal downtime.
Configuring Fabric for Optimal Performance:
Learn how to set up and configure Fabric to handle migrated data efficiently, optimizing for performance and scalability.

Querying Data in Fabric:
Explore the powerful querying capabilities of Fabric, including advanced techniques to retrieve and manipulate data effectively.
By the end of this session, attendees will have a comprehensive understanding of the migration process from SQL to Fabric, empowering them to manage and analyze their data more effectively. Whether you’re a data architect, a database administrator, or a data enthusiast, this session will provide the essential skills and knowledge to successfully transition to and leverage the capabilities of Microsoft Fabric in your data management strategies.

Seamless Data Migration to Microsoft Fabric: Strategies and Best Practices

Learn from the Microsoft product group everything new and coming for integrations between Azure SQL/SQL Server and Microsoft Fabric. Give your feedback and see lots of demos.

SQL Database and Fabric: better together

As a Microsoft SQL Server DBA, monitoring and analyzing your server’s performance and troubleshooting issues is essential. One of the most effective ways to do this is by utilizing the built-in data collection, which records a wealth of information.
In this session, we will explore six collections (Logs and traces) that can provide valuable insights into your server’s configuration, health, and performance.

• Default trace
• SQL Error log
• SQL Agent Error log
• Cluster log Always On availability group
• AlwaysOn_health
• System_health

I will show you each one's default location, retention, and content captured. We will discuss how to access and interpret these logs and demonstrate how they can be used to identify and diagnose issues such as failed logins and deadlocks, the health of the Windows cluster and availability group, and schema changes. By effectively utilizing the SQL Blackbox, you can ensure the smooth operation of your server and maintain the highest performance and reliability.

Prerequisite: Familiar with SQL Server administration

SQL Server Detective: Investigating Logs and Traces for Optimal Performance

Sustainability is a very important topic that affects the entire world. Especially in Europe where there is a strong focus on it.

Over the next few years, a lot of companies will be focusing on it even more. Due to the fact that they must report on sustainability metrics as part of the Corporate Sustainability Reporting Directive (CSRD) implemented by the EU.

You're already contributing to step one of your companies sustainability efforts by watching this event online instead of international travel. 

Now, I can help you with do step two, by showing you how Microsoft Fabric can help with your company's needs. By introducing Sustainability data solutions in Microsoft Fabric. 

Topics covered include how to:
-	Deploy the capabilities within this solution.
-	Ingest the data required for this solution. 
-	Analyze your Microsoft Azure emissions insights data  within Microsoft Fabric.
-	Process your source data to create the relevant Environmental, Social and Governance (ESG) metrics that are required for the CSRD.
-	Visualize CSRD metrics data.

Demos will be shown during the course of the session.

By the end of this session, you will know how to work with the Sustainability data solutions in Microsoft Fabric and help with your companies sustainability efforts.


Step two in helping your companies sustainability efforts with Fabric

testing speaker CFS

In this session, you'll discover how to utilize DAX INFO functions in Power BI along with Power Automate for efficient documentation of your Power BI reports and Semantic Models. in this session, I will cover:
1- Basics of DAX INFO functions.
2- Combining DAX INFO functions to extract key data
3- Scan the Power BI Tenant and find the Workspaces and Semantic Models, using the Power Automate
4- Run the DAX query using the Power Automate
5- Manage the output and save that as your Power BI tenant documentation

The Art of Power BI Tenant Documentation: Using INFO DAX Functions and Power Automate

In the fast-evolving landscape of big data, frequent system migrations pose significant challenges, particularly in code conversion and adoption of new data processing engines. This presentation addresses the inefficiencies of manual code migration processes faced by big data teams, emphasizing the need for scalability and faster adoption rates. We propose an innovative approach that utilizes Gen AI and cloud services such as AWS Bedrock, Redshift, Glue, Athena, and S3, coupled with robust automated testing mechanisms. Our methodology has proven effective, demonstrating a 75% reduction in the time and effort required for code conversion and testing during migrations. We will also explore further applications of this strategy across the big data domain, highlighting its potential to significantly enhance operational efficiency for big data teams.

The Future of Big Data with Gen AI: Accelerating System Migration and Boosting Efficiency

Unlock the full potential of your data
Supercharge Productivity
Let AI summarize actions for you, and optimize your time
Uplevel Skills 
Be better at what you’re good at and master what you’ve yet to learn 
Discover Insights
Harness the power of AI so you never have to start from scratch

The Microsoft Fabric tour in 80 copilot prompts

The fast, ever changing world of development is always bringing enhancements and improvements to the tooling that can be used for development. This session will take a tour through the new capabilities for Azure SQL Database development and show you how they can improve your development and reduce your time to deployment.

The new sqlcmd has so many more features available to enable quick efficient development of databases.

The data api builder exposes a  modern REST and GraphQL endpoints to your Azure SQL Databases.

azd ( the Azure Development CLI) can take you from development environment to Azure in a few short commands.

After this session, you will have more knowledge about this capabilities and enough content to be able to start to explore them yourself.

The new era of SQL Development -sqlcmd, data api builder,azd and more

Microsoft Fabric is Microsoft's flagship Software-as-a-Service Analytics service combining Power BI, Data Engineering, Data Warehousing, Realtime Analytics, and Machine Learning.

In this session we'll be diving into the Fabric Link for Dataverse functionality which lowers the technical barriers when allowing data in Dataverse (including Dynamics) to be analysed within Microsoft Fabric.

We'll be looking at the benefits but also what you need to look out for.

The Reality of Fabric Link for Dataverse: What You Need To Know

In the era of big data, the ability to efficiently query and analyze large datasets is paramount for businesses and researchers alike. Kusto Query Language (KQL), developed by Microsoft, has emerged as a powerful tool for querying and processing data stored in Azure Data Explorer and other Microsoft services. This session will delve into the advanced capabilities of KQL, demonstrating how it can be leveraged to extract meaningful insights from vast datasets with speed and precision.

This presentation will cover the following key areas:

Introduction to KQL: An overview of KQL, its syntax, and fundamental concepts. We will highlight its advantages over traditional SQL in terms of performance and usability for specific use cases.

Advanced Query Techniques: Demonstrating complex queries and functions that KQL offers, including time-series analysis, pattern matching, and machine learning integration. Real-world examples will showcase how these techniques can solve intricate data problems efficiently.

Optimization Strategies: Best practices for optimizing KQL queries to enhance performance and reduce resource consumption. This includes tips on indexing, caching, and query partitioning.

Use Cases and Applications: Detailed case studies from various industries, such as cybersecurity, IT operations, and business intelligence. These examples will illustrate how organizations are using KQL to drive decision-making and operational efficiency.

Hands-On Demonstration: A live demo session where participants can see KQL in action, followed by an interactive Q&A segment to address specific questions and scenarios from the audience.

Unlocking Data Insights with Kusto Query Language (KQL)

This session highlights the key capabilities of Google Cloud’s Generative AI's (GenAI) includes Vertex AI portfolio, including Vertex AI Model Garden, Vertex AI Platform, Vertex AI Search, and Vertex AI Conversation, previously use to be GenAI App Builder. It also introduces the recently launched Vertex AI Gen AI APIs and Vertex AI model garden. The session provides an overview of the different tools and services offered by the Vertex AI portfolio, how they can be used to build and deploy generative AI models, and how they can help businesses solve a variety of use cases. Also we would Unlock the potential of Vertex AI's Gen AI models through an interactive demo that showcases all the latest GenAI offerings.

Unlocking the Power of Generative AI with Google Cloud GenAI

In this discussion, George will cover the current major database-as-a-service offerings which are SQL-based, followed by an overview of Fabric, and how it can glue together your enterprise data.
Gain deep insights into the latest offerings in this space and learn how Fabric integration can revolutionize your data strategies.
This session is tailored for SQL Server and Azure data professionals.

Unveiling the Future: Dive into Cloud Databases and Analytics with Fabric

How well do you really understand data security, and are you thinking of the impact artificial intelligence (AI) has on it? Data is data regardless of where you put it, and data security is still data security, even as we explore AI’s role in data. As business leaders and security experts, our muscle memory reaction is built around traditional methods such as encryption and data masking. However, there’s a better road to increased business value and secured data in the age of AI. Some of the largest names in data security are still missing this fundamental concept, but as data leaders, we can’t continue to afford these levels of misunderstandings if we’re to leverage AI effectively.  

Anthony Cammarano will discuss these data security misunderstandings in an age of AI and educate the audience on key concepts such as:  

The rise of Large Language Models (LLMs) creates more risk and exposure of sensitive personal identifiable information (PII). By connecting sensitive data to these pipelines, you’re removing the existing data controls in place and oftentimes granting full and clear access to that data. In other words, you can no longer restrict access only within the data store. More data sharing means more exposure and more risk.  

Generative AI (GenAI) increases the surface area for misunderstandings in data security. Access and masking aren’t enough to protect data in these AI models. If you’re deploying data controls in transit and at rest, what happens while it is in use? We are just beginning to understand the data flow associated with these models, along with the stages of AI development and deployment that each carry unique risks at their respective levels.  

There is more nuance to data security and protection than ever as we explore AI’s role across tech stacks. By discussing existing misunderstandings and pitfalls in data security, we can begin to uncover better business value in AI without compromising sensitive information.  

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Community Days | Future Data Driven Summit 2024