Luca Zavarella
Microsoft MVP, Head of Data & AI at iCubed
Microsoft MVP, Head of Data & AI in iCubed
Milan, Italy
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Luca holds a degree in Computer Engineering from the Faculty of Engineering of the University of L'Aquila and has more than fifteen years of experience working on the Microsoft Data Platform. He started his experience as a T-SQL developer on SQL Server 2000 and 2005. He then focused on the whole Microsoft Business Intelligence stack (SSIS, SSAS, SSRS), deepening data warehousing techniques. Recently, he has been focusing on the world of Advanced Analytics and Data Science. He contributes to the technical community as a speaker at data events and through his personal blog on Medium. He is the author of "Extending Power BI with Python and R 2nd Ed.", published by Packt Publishing. He holds the role of Data & AI Solution Hub Lead at Lodestar. He is currently a Microsoft AI & Data Platform Most Valuable Professional (MVP).
He also graduated with honors in classical piano from the "Alfredo Casella" Conservatory in L'Aquila.
Luca si è laureato in Ingegneria Informatica presso la Facoltà di Ingegneria dell'Università dell'Aquila e ha più di quindici anni di esperienza di lavoro sulla piattaforma dati Microsoft. Ha iniziato la sua esperienza come sviluppatore T-SQL su SQL Server 2000 e 2005. Si è poi concentrato sull'intero stack Microsoft Business Intelligence (SSIS, SSAS, SSRS), approfondendo le tecniche di data warehousing. Recentemente si è concentrato sul mondo dell'Advanced Analytics e della Data Science. Contribuisce alla comunità tecnica come speaker in eventi sui dati e attraverso il suo blog personale su Medium. È autore di "Extending Power BI with Python and R, 2nd Ed.", pubblicato da Packt Publishing. Ricopre il ruolo di Data & AI Solution Hub Lead in Lodestar. Inoltre, è Microsoft AI & Data Platform Most Valuable Professional (MVP).
Si è inoltre diplomato con lode in pianoforte classico presso il Conservatorio "Alfredo Casella" dell'Aquila.
Area of Expertise
Topics
How regexes in Power BI using Python and R can save your life in extreme cases en
There are cases where cleaning the data provided by a data source requires advanced techniques that are not available by default in the arsenal included in Power Query. In this session we will look at how the use of regular expressions (regex) in Power BI can solve data cleaning situations that are impossible at first glance.
Simplifying ChatGPT: Efficient Document Querying with Azure OpenAI en
This session aims to demystify ChatGPT for a broad audience, highlighting its integration with Azure OpenAI for effective document querying. We'll cover the basics of ChatGPT, its language model, and how to set it up in Azure, focusing on usability, data security, and compliance. Real-world examples will demonstrate its practical applications in extracting insights from data, with the goal of equipping attendees with the knowledge to effectively use ChatGPT in various scenarios.
Enhancing Data Analysis: Leveraging SQL Server's Extensibility Framework with Power BI en
This session focuses on the integration of SQL Server's extensibility framework with Power BI to supercharge your data analytics. We will delve into how to configure and use Python and R engines within SQL Server and explore their applications within Power BI. The discussion will highlight the setup process, the advantages of in-database analytics, and real-world scenarios where this integration shines, particularly in overcoming the limitations of Power BI’s native capabilities. By the end of this session, attendees will have a comprehensive understanding of how to leverage SQL Server's machine learning services to optimize their data analytics workflows in Power BI, ensuring enhanced performance, data security, and usability.
Ask Your Data: Fabric Data Agents in Action en
Fabric Data Agents allow you to ask natural language questions directly to data wherever it resides, whether in OneLake (lakehouse), the Data Warehouse, Power BI semantic models, or even KQL databases. In this session, we'll explore how the agent leverages user credentials (via Microsoft Entra) to query only the data scopes to which it has access, interpret the question, and translate it into queries.
We'll also see how to guide the agent with custom prompts and contextual rules that drive response quality.
An end-to-end demo will move from the authorized dataset to the generated response, demonstrating the complete step-by-step flow.
Ask Your Data in Italian: Fabric Data Agent in Action en it
Fabric Data Agents enable users to interact with their data through natural language across multiple sources, including Lakehouses, Warehouses, SQL and KQL databases, Power BI semantic models, Graph, ontologies, and content indexed through Azure AI Search.
In this session, we will explore how the agent uses the user’s credentials to access only authorized data, identify the most appropriate source, interpret the question, and translate it into the corresponding query.
We will also demonstrate how to enrich a Data Agent with targeted instructions, descriptions, and example queries to guide its behavior and improve the quality and accuracy of its answers.
Finally, we will address one of the product’s current limitations. Although Microsoft documentation states that English is the only officially supported language, we will show how to configure a Fabric Data Agent so that it can understand questions and return answers in Italian.
An end-to-end demo will complete the journey, from configuring the data sources to generating a ready-to-use answer in Italian.
Chiedilo ai tuoi dati in italiano: Fabric Data Agent in azione en it
I Fabric Data Agent permettono di interrogare i dati in linguaggio naturale attraverso diverse sorgenti, tra cui Lakehouse, Warehouse, database SQL e KQL, Semantic Model di Power BI, Graph, ontologie e contenuti indicizzati tramite Azure AI Search.
In questa sessione vedremo come l’agente utilizza le credenziali dell’utente per accedere esclusivamente ai dati autorizzati, individuare la sorgente più adatta, interpretare la domanda e trasformarla nella query appropriata.
Mostreremo inoltre come arricchire il Data Agent con istruzioni mirate, descrizioni e query di esempio per guidarne il comportamento e migliorare la qualità e l’accuratezza delle risposte.
Affronteremo infine una delle attuali limitazioni del prodotto: sebbene la documentazione Microsoft indichi l’inglese come unica lingua ufficialmente supportata, vedremo come configurare il Fabric Data Agent affinché possa comprendere le domande e restituire le risposte in italiano.
Una demo end-to-end completerà il percorso, dalla configurazione delle sorgenti dati alla generazione di una risposta in italiano pronta per essere utilizzata.
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