Microsoft Reactor Toronto

 
 
 

Azure Cosmos DB is a fully managed NoSQL database, offering different APIs and scalable across the globe. You can use it as a document database, where you store many different JSON files for example.
But what happens when you need to run large analytical queries on top of your Cosmos DB? If the data is partitioned differently than your query access path, you might end scanning the whole thing?

In this session, we will walk you through the concept of the analytical store on Cosmos DB, and how this can work together with the Azure Synapse Link feature of Azure Synapse to enable painless analytics on your data. We will show you how you can use Serverless SQL and Spark Pools to query the data. At the end, you should be able to assess if this solution will be a good match for your use cases.

 
 
 

About this session:
In this interactive session, we will explore the core concepts of Terraform, a popular Infrastructure as Code (IaC) tool, and learn how to use it to deploy a static web application on Azure. The session will be a mix of theory and hands-on exercises, ensuring participants gain practical experience along with conceptual understanding.

Who is it aimed at?
Developers and DevOps engineers interested in Infrastructure as Code (IaC) and deploying static web applications on Azure.

Why should I attend?
If you are looking for an interactive session on Infrastructure as Code with Terraform on Azure, this is exactly the session. Here are some learning objectives: Understand the core concepts of Infrastructure as Code (IaC) and the role of Terraform in IaC. Understand the main components of a Terraform configuration. Write, plan, and apply a basic Terraform configuration for Azure. Deploy a static web application to Azure using Terraform. Understand the best practices for managing infrastructure with Terraform.

Here are some useful modules for this session:
https://aka.ms/June7TerraformOverview3
https://aka.ms/June7TerraformCloudShellBash3

 
 
 

Acerca de la serie: Bits of Build
¡Únase a nosotros para Bits of Build, una increíble serie de eventos inspirados en Microsoft Build! Microsoft Build es una de las conferencias de tecnología más grandes del mundo para desarrolladores y profesionales de la tecnología interesados en conocer los últimos lanzamientos de tecnología de Microsoft en áreas como DevOps, AI, Low Code y más.

En esta reunión de Bits of Build, estamos reuniendo a miembros de la comunidad tecnológica para compartir sus aprendizajes sobre: Introducción a los conectores personalizados

¡No te pierdas Microsoft Build 2023! Aplicar: aka.ms/build
Únase al Desafío de habilidades en la nube: ka.ms/BuildCSC
Conéctese con la comunidad de expertos de Microsoft Learn: aka.ms/MicrosoftLearnCommunity

Únase a las futuras sesiones de esta serie: https://aka.ms/BitsOfBild-Espanol

Acerca de esta sesión:
En determinados casos los desarrolladores de apps y flujos (makers) necesitan acceder a otros datos u aplicaciones que se encuentran en la nube. Así, mediante los conectores personalizados podremos integrar los componentes de la Power Platform con otros sistemas. El objetivo de la sesión es mostrar el proceso de creación y publicación de un conector personalizado, y como éste se puede utilizar en cualquier flujo de Power Automate o aplicación de Power Apps.

Aquí hay un recurso útil para esta sesión:
https://aka.ms/Junio8LearnConectorsPersoPowerApps3

 
 
 

We kicked off week four of our Machine Learning for beginners learning cohort by sending you to a brand-new series of videos covering logistic regression. In this session, we will be working through the content live, so you can dive deeper into your ML journey.

Join us as we introduce logistic regression and build a Jupyter Notebook to classify pumpkin colors using logistic regression.

Missed the videos? Catch up at https://aka.ms/ml-beginners-videos. Bring your questions to this session or come chat with us on the Microsoft Python Discord server. Join at http://aka.ms/python-discord-invite if you haven’t joined already and ask your questions on the #machine-learning channel.