Christian Wade joins Scott Hanselman to show you how to unlock petabyte-scale datasets in Azure with a way that was not previously possible. Learn how to use the aggregations feature in Power BI to enable interactive analysis over big data.

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Gaurav Malhotra joins Scott Hanselman to discuss the Azure Data Factory visual tools, which enable you to iteratively create, configure, test, deploy, and monitor data integration pipelines. We took into account your feedback to enable functional, performance, and security improvements to the visual tools.

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Gaurav Malhotra joins Scott Hanselman to discuss Azure Data Factory (ADF) integration with Azure Monitor, which enables you to route your data factory metrics to Operations and Management Suite (OMS). Get the Azure Data Factory Analytics OMS service pack from the Azure marketplace.

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Carey MacDonald stops by to chat with Scott Hanselman about Azure Cosmos DB, a managed database service with unlimited horizontal scale, allowing you to scale storage and throughput independently.

In this video, Carey will cover how to partition your data in Azure Cosmos DB, best practices for choosing a partition key, and how to troubleshoot bad partition key choices.

I’ve long been a fan of integrating maps into applications, especially given how easy it is to get started. But there’s a lot more to mapping than just figuring out where the nearest Starbucks is.

Learn about the cartography design process as well as the robust data behind the curtains.

 

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Today’s business managers depend heavily on reliable data integration systems that run complex ETL/ELT workflows (extract, transform/load and load/transform data).

Gaurav Malhotra joins Scott Hanselman to discuss how you can iteratively build, debug, deploy, and monitor your data integration workflows (including analytics workloads in Azure Databricks) using Azure Data Factory pipelines.

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Aravind Krishna stops by to chat with Scott Hanselman and take a look at common design patterns for building highly scalable solutions with Azure Cosmos DB.

The get into modeling data and how to choose an appropriate partition key. Then they look at a few patterns like event sourcing, time series data, and patterns for addressing bottlenecks/hot spots for reads, writes, and storage.