Name: Data Engineering in Fabric | Query Azure SQL using Natural Language (In Person)
Start: 2023-11-01T18:15:00Z
End: 2023-11-01T21:00:00Z
Location: Blue Boar House

***This is the link to register to attend in person. To register to attend online, please use*** [Data Engineering in Fabric \| Query Azure SQL using Natural Language \(Online\)](https://www.meetup.com/oxford-microsoft-data-platform-group/events/296308910)

18:15- 18:30 - Networking and Refreshments
18:30: **Data Engineering an End-to-End Solution in Microsoft Fabric** – Ed Freeman
19:30: Break
19:45: **Query your Azure SQL Data using Natural Language** \- Adam Buckley
20:45: Prize draw

**Session details:**
**1\. Data Engineering an End\-to\-End Solution in Microsoft Fabric**
Fabric has all these new, exciting, and intelligent features in the "unified analytics product to rule them all". So what? You're still tasked with building a real, end-to-end platform transcending much more than just any single "cool" piece of functionality. In this session we'll go through a full E2E scenario (from defining goals, architecting the "Lakehouse" solution, all the way through to operational monitoring), touching on many elements required to engineer a robust and scalable solution in Microsoft Fabric. We'll be using public Land Registry data provided by the UK Government to drive the demos, which comprises just shy of 30 million rows of House Price Paid data from the last 30 years.

**2\. Query your Azure SQL Data using Natural Language**
Are you a database developer who wants to get in on all this ChatGPT action? Maybe you want to integrate a large language model with your database without writing Python code? How about if you could do this with just simple T-SQL statements? In this session we'll dive into tsql-chatgpt which integrates an Azure SQL database with ChatGPT using just T-SQL. Automate documentation, unit testing, generate test data and more including turning natural language into SQL queries on your data.

Bio:
**Ed Freeman**
Ed is a Data Engineer at endjin who has been implementing intelligent cloud & data platforms built on Microsoft's cloud for clients of all sizes and industries for many years. He is particularly passionate about Power BI, Fabric and Microsoft data platform services as a whole, and has spoken at international conferences on such topics. He also blogs, creates video content and heads-up Power BI Weekly, a free weekly newsletter to help the community keep up to date with the latest and greatest developments from the Power BI ecosystem.
[https://endjin.com/who-we-are/our-people/ed-freeman/](https://endjin.com/who-we-are/our-people/ed-freeman/)
[https://powerbiweekly.info/](https://powerbiweekly.info/)

**Adam Buckley**
Adam is an experienced leader in Data and Software Engineering, working with Microsoft Data & AI technologies over the last 30 years for many organisations including Logica, Nike, Transunion and most recently Microsoft. Adam has previously published apps for Apple's iPhone and iPad and now focuses on helping customers migrate large on-premise workloads to the Azure platform.
Adam’s blog posts:
[https://www.linkedin.com/pulse/automate-documentation-testing-azure-sql-using-chatgpt-adam-buckley/](https://www.linkedin.com/pulse/automate-documentation-testing-azure-sql-using-chatgpt-adam-buckley/)
https://www.linkedin.com/pulse/query-your-data-azure-sql-using-natural-language-chatgpt-adam-buckley/

Robert Minchin

Oxford Microsoft Data Platform Group

Azure Data Tech Groups

Prize draw sponsor - giving away two ebooks at each event

Packt

Sourcing Software Engineering talent for tech start-ups and scale-ups

Humand Talent

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Database Professionals

Business Intelligence & Data Warehousing

Business Intelligence in Cloud

Microsoft Azure

Business Intelligence Tools

Database Development

Big Data

Data Integration

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Apache Spark

Data Management

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Data Analytics

SQL Azure

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Data Engineering in Fabric | Query Azure SQL using Natural Language  (In Person)

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Oxford Microsoft Data Platform Group

Data Engineering in Fabric | Query Azure SQL using Natural Language (In Person)

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