Introduccion a Github Copilot

En esta sesión, exploraremos GitHub Copilot, la herramienta de inteligencia artificial que potencia la productividad de los desarrolladores. Comenzaremos con los fundamentos de los modelos de lenguaje (LLM), el concepto de tokens y las bases del prompt engineering. Analizaremos las principales características de Copilot, su integración con los IDEs más populares y el impacto que tiene en la generación de código, documentación y pruebas automatizadas.

Veremos técnicas de prompt engineering, incluyendo estrategias como zero-shot, one-shot y few-shot prompting, así como el razonamiento chain-of-thought para obtener resultados óptimos.
Finalizaremos con un espacio de preguntas y respuestas para resolver dudas y compartir experiencias sobre el uso de GitHub Copilot en el desarrollo profesional.

DAB Essentials: Building Dynamic APIs for Azure SQL DB

In this session we are going to review on how to create on- demand APIs, how to create scalable solutions with Azure SQL DB and Data API Builder (DAB) as a tool set extending capabilities for your database.

Let's create easy APIs to connect our database with our applications.

We are going to review the API interfacing and GraphQL and what differences are from the relational perspective of the relationship of the data use.

All of these actions we are going to do using VS Code and extensions to interface with DB as well as Azure Web Static Apps

Working with Azure SQL for Azure DevOps

Summary:

In this session we will work on Azure SQL as a data platform working interacting in collaboration with different teams in Azure Devops accelerating value of your workflow. Implementing DevOps practice helps organizations to develop en deliver faster products, maintaining the stability and confidence, increasing confidence in the applications they build, and achieve business goals faster..

In this session we can explore the use of tools and platforms like Visual Studio, Azure Data Studio, Git and Azure DevOps for a modern platform as data ops role using with DevOps practice in Azure SQL.
With the combination of these platforms we can experience the integration of data world into the DevOps culture providing tools for the data engineer to support the process of delivering applications.

Using WSL2 for the database development platform

In this session, we will apply (WSL) Windows Subsystem on Linux to manage SQL Server on Docker containers interfacing in your local environment.
Goal1:

Overview of the WSL configuration and interfacing between Windows OS host as well as most common libraries in

Goal2:

Overview SQL Server containers demos inside the image prepared as well as the configuration for it.

Goal3:

Have a look for automated scripts created to interact between Linux and windows to perform interfaced tasks.

Abstract:

As technology evolves Windows Subsystem for Linux has too, after many years now we can see that there is a fully integrated cross-platform inside the Windows machine host. Now we are seeing that Docker is now working for the Windows Subsystem for Linux (WSL).

In this session, we will take advantage of this new out-brake to maximize the use of resources and apply this scenario into the SQL Server world with the use of containers. We will manage a containerized SQL Server database and inspect through scripting capabilities between Linux and windows environments working together.

Integrando Soluciones stateful con WSL2 en el desarrollo de DB’s

En esta sesión, aplicaremos el subsistema de Windows (WSL) en Linux para administrar SQL Server en contenedores que interactúan completamente en su entorno local para lograr manejar una integración en un escenario statful.

Working with Azure SQL DB Serverless

Abstract:

Summary: In this session we will be working with Azure SQL DB, we will be exploring
the use of the compute model serverless. Using the Azure CLI, and the declarative mode we will be
creating, deleting and altering the server and databases. All of this with the help of Visual Studio Code
integrating with different technologies to make SQL Server better.

Goal 1: Provisioning the server with the serverless compute model
Goal 2: Creating and altering db using declarative model through Azure CLI
Goal 3: Creating a client that consumes the database using serverless function built in python

In this session we will be working with Azure SQL DB, we will be exploring serverless model, VSCode, AzureCLI.