<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>
<div style="display: flex;">
    <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>
</div>
<p></p>

Future Data Driven Summit 2024

Room v4

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

Twitter

LinkedIn

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

X (Twitter)

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

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

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

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

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

Facebook

In today’s data-driven landscape, delivering a compelling data solution to customers is crucial. This session will guide you through the intricacies of crafting a data solution that not only meets the technical requirements but also aligns with your customer’s business objectives. We’ll explore the art of translating complex data concepts into actionable insights that resonate with stakeholders. Attendees will leave with a clear understanding of how to approach data solution proposals, ensuring they are both innovative and practical. Whether you’re dealing with data integration, migration, or analytics, this session will provide you with the tools to make your proposal stand out. Join us to elevate your data solution strategies and captivate your audience!

How to propose a data solution to your customer

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)

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!