Dr. Ranveer Chandra gives Scott Hanselman an introduction to Azure FarmBeats at Microsoft Ignite 2019.

FarmBeats is a business-to-business offering available in Azure Marketplace. It enables the aggregation of data from farms — across sensors, drones, robots, satellites, and weather providers — and generation of actionable insights using artificial intelligence (AI) and machine learning (ML) models built on fused datasets.

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Tim Sander joins Scott Hanselman to discuss composite indexes and correlated subqueries using the SQL API in Azure Cosmos DB.

A Request Unit, or RU, is the measure of throughput in Azure Cosmos DB. Learn how to optimize queries with a composite index to decrease the RUs needed for a given query.

They also showcase correlated subqueries and examples of how they can make it easier to query arrays in Azure Cosmos DB.

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              Learn how you can publish your SaaS solution and start generating revenue by making it available to potential buyers on Microsoft AppSource and Azure Marketplace.

              Ercenk Keresteci joins Scott Hanselman to cover the technical details of integrating a solution with Microsoft’s commercial marketplace, including how to use scripts for provisioning and de-provisioning a customer.

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              Keiko Harada joins Scott Hanselman to show how to use Prometheus to monitor containers in Azure Monitor.

              Prometheus is a popular open source metric monitoring solution and is a part of Cloud Native Compute Foundation.

              Customers who like the extensive metrics which Prometheus provides on Kubernetes also like how easy it is to use Azure Monitor for containers which provides fully managed, out of the box monitoring for Azure Kubernetes Service (AKS) clusters.

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              Christina Lee joins Scott Hanselman to show what’s new in Azure Cognitive Services.  Cognitive Services bring AI within reach of every developer—without requiring machine-learning expertise.  All it takes is an API call to embed the ability to see, hear, speak, search, understand, and accelerate decision-making into your apps.

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              Most of the time, your apps are running happily and healthily.

              However, when things go wrong, you can use Genie and Navigator in App Service Diagnostics to figure out what’s wrong.

              Jen Lee joins Scott Hanselman to show how the App Service Diagnostics interactive interface (Genie) guides you intelligently through the self-troubleshooting experience. Navigator, enables you to find potentially breaking changes to your app and its dependencies.

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              Gaurav Malhotra joins Scott Hanselman to show how wrangling data flows in Azure Data Factory.

              This provides  a code-free, serverless environment that simplifies data preparation in the cloud and scales to any data size with no infrastructure management required.

              It uses the industry-leading Power Query data preparation technology (also used in Power Platform dataflows, Excel, and Power BI) to prepare and shape the data. Built to handle all the complexities and scale challenges of big data integration, wrangling data flows enable use Apache Spark execution to help you easily prepare data at scale.

              Aman Bhardwaj and Yaron Schneider join Scott Hanselman to talk about the core concepts of Distributed Application Runtime (Dapr).

              Dapr is a portable, event-driven runtime that makes it easy for developers to build resilient, microservice stateless and stateful applications that run on the cloud and edge and embraces the diversity of languages and developer frameworks     

              Dapr Resources

              Using a bastion host can help limit threats such as port scanning and other types of malware targeting your VMs.

              Ashish Jain joins Scott Hanselman to show how Azure Bastion gives you secure and seamless RDP and SSH access to your virtual machines.

              Now you can securely access your VMs over SSL from the Azure portal and without exposing public IP addresses.

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                Gaurav Malhotra joins Scott Hanselman to show how you can run your Azure Machine Learning (AML) service pipelines as a step in your Azure Data Factory (ADF) pipelines.

                This enables you to run your machine learning models with data from multiple sources (85+ data connectors supported in ADF).

                This seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.     

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