23 videos
6:26
This video walks through the process of setting up Microsoft Fabric database mirroring from Google BigQuery, including an automated script for configuring the GCP side. You'll learn how to create a mirrored database in Fabric, set up near real-time replication, and validate the data synchronization between BigQuery and Fabric.
5:20
This video walks through how to use Microsoft Fabric shortcuts to access Google BigQuery Iceberg tables, automating the setup process on the Google Cloud side and configuring direct SQL queries within Fabric without copying or moving data. You'll learn about the automation script that handles service account creation, IAM permissions, and required BigQuery table access, followed by setting up connections and creating shortcuts in Fabric for seamless data querying.
33:16
This video walks through the process of building an end-to-end analytics reference architecture for the National Football League (NFL) using Microsoft Fabric. Viewers will learn how to download and process NFL data, create a Fabric workspace and Lakehouse, deploy Power BI semantic models, and develop AI-powered analytics agents, all while exploring key concepts like data modeling and agent evaluation workflows.
7:29
This video walks through the process of exposing on-premises SQL Server data for natural language querying using Microsoft Fabric Mirroring and OneLake. You'll see how to set up Change Data Capture (CDC) and the On Premises Data Gateway, mirror specific tables into OneLake, and use a Fabric Data Agent to enable users to ask plain English questions that are then processed by Fabric without manual SQL intervention.
17:22
This video walks through configuring a Microsoft Fabric workspace with a Workspace-scoped Private Link, demonstrating how to restrict access to an Azure Virtual Network (VNet) and build a secure analytics architecture that combines strong network isolation with flexible reporting access. Viewers will learn about the setup process, including creating virtual networks, subnets, private endpoints, and deploying a jump box VM for testing private connectivity.
4:31
This video walks through the process of building a Microsoft Fabric ontology using Python code within a Fabric notebook. You'll learn how to define entities, relationships, and properties using Python, as well as automate the deployment process via the Fabric REST API.
1:55
This video walks through the integration of a Microsoft Fabric Data Agent with a Microsoft Foundry Agent to create a versatile multi-source AI assistant. Viewers will learn how to combine structured enterprise data from a Fabric Ontology with unstructured documents using Azure AI Search, enabling the agent to answer complex questions and support natural language queries across various knowledge sources.
4:23
This video walks through building a secure, production-ready web application using Microsoft Fabric, Fabric Ontology, and Microsoft Foundry Agents to integrate map-based visualizations and generative AI experiences within a custom React app. The walkthrough covers the architecture of the solution, including the React frontend with Next.js, FastAPI Python backend, and various Azure services like AI Search and Maps.
41:08
This video walks through the process of designing and building an ontology in Microsoft Fabric, covering key aspects such as defining entity types and relationships, applying best practices for relationship naming, and implementing these concepts step-by-step within the platform. Viewers will gain insights into what an ontology is and how it can be used for data analysis in a business context.
9:04
This video walks through the basics of graph data analysis and demonstrates how Microsoft Fabric's new graph capabilities can be used to build and query graphs, focusing on practical examples using customer and order data. Viewers will learn about graph models, nodes, edges, and relationships, as well as how to use GQL for querying graphs within a Fabric Notebook.
11:34
This video walks through the process of using Azure AI Vision to generate descriptive metadata for images, and then integrating this data into a Power BI analysis solution. Viewers will see a live demonstration of creating an enriched image dataset and how it can be visualized in Power BI.
7:27
This video walks through the process of integrating a Microsoft Fabric Semantic Model query into a custom web application built with React and TypeScript, covering authorization, token fetching, and querying semantic models using the Power BI REST API. Viewers will learn how to add necessary permissions, fetch access tokens, and implement code within their React app to submit queries and display results.
7:03
This video walks through the process of using Fabric mirroring to replicate data from a Snowflake Data Warehouse into a Microsoft Fabric Data Warehouse, focusing on enabling mirroring with Change Data Capture for near real-time data replication. Viewers will see how to create and manage mirrors, select tables, monitor mirror status, and review and add new data.
5:27
This video walks through the process of using a Fabric pipeline to import data from a Snowflake Data Warehouse into a Lakehouse Delta Table, demonstrating how to create a pipeline, establish a connection with Snowflake, select the appropriate data, and run the pipeline to ensure successful integration.
6:23
This video walks through the process of exporting a machine learning model developed in Microsoft Fabric, importing it into an Azure Machine Learning workspace, and deploying it to a real-time inference endpoint. Viewers will learn how to set up and test this deployment, ensuring their models can provide real-time predictions efficiently.
13:30
This video walks through the process of creating and installing custom Python libraries in a Microsoft Fabric Lakehouse environment, demonstrating how to package code for reuse across notebooks. Viewers will learn how to create a Python package that fetches secrets from Azure Key Vault and install it within their Fabric workspace.
8:43
This video walks through using Azure OpenAI Services within a Fabric Jupyter notebook to automatically summarize large sets of semi-structured user reviews. It also covers prompt engineering, secure secret storage in Azure Key Vault, and data handling with Delta tables in a Fabric Lakehouse environment.
15:31
This video walks through the process of building a Document Intelligence solution using Azure AI within Microsoft Fabric. It demonstrates how to ingest scanned document data into a Data Lake solution by leveraging Spark Notebook and Synapse ML for text extraction.
5:42
This video walks through the process of integrating Azure AI Translation into a Fabric Data Lake solution using Synapse ML features. Viewers will learn how to apply Language AI Services within Python Jupyter notebooks stored in a Fabric workspace for data enrichment and transformation.
9:01
This video walks through setting up and using Git repositories with Microsoft Fabric Workspaces, covering configuration, syncing notebooks and other content, and how to recover unintended changes. You'll learn to connect a Git repository to your workspace, commit changes, and revert modifications as needed.
4:26
This video walks through the process of displaying a Power BI report directly within a Jupyter notebook hosted on Microsoft Fabric using the Power BI Python client. Viewers will learn how to authenticate, import necessary dependencies, and create a report instance in just a few steps.
9:54
This video walks through how to use Fabric Semantic Link to integrate Power BI as a data source for developing machine learning models. Viewers will see a step-by-step guide on connecting to Power BI measures, querying the data, and training an ML model within Microsoft Fabric.
6:26
This video walks through the process of moving a local Jupyter notebook from Anaconda to Microsoft Fabric Data Science, using a dataset on SpaceX launches to predict successful missions. Viewers will learn how to leverage Fabric Data Science without needing prior knowledge of developing machine learning models.