Merge pull request #51104 from weslbo/synthesia-video-poc

added videos to learn unit

Author: prmerger-automator[bot]

learn-pr/wwl-data-ai/explore-core-data-concepts/includes/1-introduction.md

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+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=a58ad727-a545-40b1-9998-68718a8b9ae3]
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 Over the last few decades, the amount of data generated by systems, applications, and devices increased significantly. Data is everywhere, in a multitude of structures and formats.
 
 Data is now easier to collect and cheaper to store, making it accessible to nearly every business. Data solutions include software technologies and platforms that can help facilitate the collection, analysis, and storage of valuable information. Every business would like to grow their revenues and make larger profits. In this competitive market, data is a valuable asset. When analyzed properly, data provides a wealth of useful information and informs critical business decisions.

learn-pr/wwl-data-ai/explore-core-data-concepts/includes/2-data-formats.md

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+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=8d989e55-82f8-4d3a-955e-40774d21a4a8]
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 Data is a collection of facts such as numbers, descriptions, and observations used to record information. Data structures in which this data is organized often represent *entities* that are important to an organization (such as customers, products, sales orders, and so on). Each entity typically has one or more *attributes*, or characteristics (for example, a customer might have a name, an address, a phone number, and so on).
 
 You can classify data as *structured*, *semi-structured*, or *unstructured*.

learn-pr/wwl/use-dataflow-gen-2-fabric/includes/1-introduction.md

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 ms.custom:
   - build-2023
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+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=3c009694-5f3d-4649-9825-7acd7c2dc1e6]
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 Microsoft Fabric offers a unified solution for data engineering, integration, and analytics. A crucial step in end-to-end analytics is data ingestion. Dataflows Gen2 are used to ingest and transform data from multiple sources, and then land the cleansed data to another destination. They can be incorporated into data pipelines for more complex activity orchestration, and also used as a data source in Power BI.
 
 Imagine you work for a retail company with stores across the globe. As a data engineer, you need to prepare and transform data from various sources into a format that is suitable for data analysis and reporting. The business requests a semantic model that consolidates disparate data sources from the different stores. Dataflows Gen2 allow you to prepare the data to ensure consistency, and then stage the data in the preferred destination. They also enable reuse and make it easy to update the data. Without a dataflow, you'd have to manually extract and transform the data from every source, which is time-consuming and prone to errors.

learn-pr/wwl/use-dataflow-gen-2-fabric/includes/2-dataflows-gen-2.md

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 ms.custom:
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+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=c7fec645-bd40-4aa3-8ed4-c9877ef0e505]
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 In our scenario, you need to develop a semantic model that can standardize the data and provide access to the business. By using Dataflows Gen2, you can connect to the various data sources, and then prep and transform the data. You can land the data directly into your lakehouse or use a data pipeline for other destinations.
 
 ## What is a dataflow?

learn-pr/wwl/use-dataflow-gen-2-fabric/includes/3-explore-dataflows-gen-2.md

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 ms.custom:
   - build-2023
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+> [!VIDEO https://learn-video.azurefd.net/vod/player?id=3217592e-d010-4686-b4a4-36fe442fd30b]
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 In Microsoft Fabric, you can create a Dataflow Gen2 in the Data Factory workload or Power BI workspace, or directly in the lakehouse. Since our scenario is focused on data ingestion, let's look at the **Data Factory** workload experience. Dataflows Gen2 use Power Query Online to visualize transformations. See an overview of the interface:
 
 ![Screenshot of the Power Query Online interface.](../media/power-query-online-overview.png)