├── .github └── workflows │ └── pages.yml ├── CODE_OF_CONDUCT.md ├── Gemfile ├── Gemfile.lock ├── LICENSE ├── README.md ├── SECURITY.md ├── SUPPORT.md ├── _config.yml ├── _includes └── components │ └── breadcrumbs.html ├── docs ├── Ex_00 │ ├── 0001.md │ ├── 0002.md │ ├── 0004.md │ └── Ex00.md ├── Ex_01 │ ├── 0101.md │ ├── 0102.md │ ├── 0103.md │ └── Ex01_getting_started.md ├── Ex_02 │ ├── 0201.md │ ├── 0202.md │ └── Ex02_create_workspace.md ├── Ex_03 │ ├── 0301.md │ ├── 0302.md │ └── Ex03_establish_connectivity.md ├── Ex_04 │ ├── 0401.md │ ├── 0402.md │ ├── 0403.md │ ├── 0404.md │ └── Ex04_implement_medallion_architecture.md ├── Ex_05 │ ├── 0501.md │ ├── 0502.md │ └── Ex05_serving_data.md ├── Ex_06 │ ├── 0601.md │ ├── 0602.md │ ├── 0603.md │ └── Ex06_create_pbi_reports.md ├── Ex_07 │ ├── 0701.md │ ├── 0702.md │ └── Ex07_use_powerbi_copilot.md ├── media │ ├── instructions240153 │ │ ├── ArchitectureDiagramMFADBNew.png │ │ ├── architectureDiagramFabricADB.png │ │ ├── architectureDiagramMFADB.png │ │ ├── task-0.1.png │ │ ├── task-0.2.png │ │ ├── task-0.3.png │ │ ├── task-0.4.png │ │ ├── task-00.png │ │ ├── task-1.1.1.2.png │ │ ├── task-1.1.1.3.png │ │ ├── task-1.1.1.4.png │ │ ├── task-1.1.1.5.png │ │ ├── task-1.1.1.6.png │ │ ├── task-1.1.1.7.png │ │ ├── task-1.1.1.png │ │ ├── task-1.2.2.png │ │ ├── task-1.3-notebook-10.png │ │ ├── task-1.3-notebook-15.png │ │ ├── task-1.3-notebook-18.png │ │ ├── task-1.3-notebook6.png │ │ ├── task-1.3.1.png │ │ ├── task-1.3.2.png │ │ ├── task-2.1.7.png │ │ ├── task-2.1.new7.png │ │ ├── task-2.3-sql4.png │ │ ├── task-2.3-sql44.png │ │ ├── task-2.3.2.png │ │ ├── task-2.3.4.png │ │ ├── task-3.1.10.1234.png │ │ ├── task-3.1.10.png │ │ ├── task-3.1.7.png │ │ ├── task-3.1.8.png │ │ ├── task-3.1.9.png │ │ ├── task-3.3.1..9.png │ │ ├── task-3.3.1.9.png │ │ ├── task-4.1.warehouse-10.png │ │ ├── task-4.1.warehouse-16.1.png │ │ ├── task-4.1.warehouse-16.png │ │ ├── task-4.1.warehouse-19.png │ │ ├── task-5.1.2.png │ │ ├── task-5.1.3.png │ │ ├── task-5.2.10.png │ │ ├── task-5.2.2.png │ │ ├── task-5.2.5-5.png │ │ ├── task-6.1.4.png │ │ └── task-6.1.6.png │ ├── instructions249094 │ │ ├── 03aobm9w.png │ │ ├── 0faouzwm.png │ │ ├── 16rkrjac.png │ │ ├── 1c9ahwzb.png │ │ ├── 3nryhf8b.png │ │ ├── 3so5uj54.png │ │ ├── 74avjs6q.png │ │ ├── 7ochejuk.png │ │ ├── a549ve6b.png │ │ ├── bv840ccs.png │ │ ├── csbraog0.png │ │ ├── ddjrz6wt.png │ │ ├── dsm8974d.jpg │ │ ├── euxmwptl.png │ │ ├── fgj24jdq_NewWorkspaces.png │ │ ├── gjqu2je6.png │ │ ├── gjt7os57.png │ │ ├── hx3j65qn.jpg │ │ ├── i511kdbu.png │ │ ├── i6p8f9yw.jpg │ │ ├── jw0pxwv3.png │ │ ├── nhl4n957.png │ │ ├── p5rdve1w.png │ │ ├── q1batkyd.png │ │ ├── qu1ki8rb.png │ │ ├── rj2lxae6.png │ │ ├── rzy3daan.png │ │ ├── sz02r32u.png │ │ ├── t8z8xytk.png │ │ ├── tmiurk6q.png │ │ ├── ub6haecs.png │ │ ├── v8c09uur.png │ │ ├── x9e8ndqe.png │ │ └── zjb7nbqd.png │ ├── instructions254096 │ │ ├── 2ndresult.jpg │ │ ├── 3lakehouses.jpg │ │ ├── addData.jpg │ │ ├── addRoleassign.jpg │ │ ├── adlsgen2.jpg │ │ ├── allowfirewall.jpg │ │ ├── azure-kv-scope.png │ │ ├── azure-kv.png │ │ ├── bronzelakehouse.jpg │ │ ├── clustercomplete.jpg │ │ ├── completedtransfer.jpg │ │ ├── configure.jpg │ │ ├── connectdbtoadls.jpg │ │ ├── containersCreate.jpg │ │ ├── containersCreatedshow.jpg │ │ ├── copilot.jpg │ │ ├── copilotgetstarted.jpg │ │ ├── copyData.jpg │ │ ├── copysucceeded.jpg │ │ ├── createNotebook.jpg │ │ ├── createkeyvault.jpg │ │ ├── createpolicy.jpg │ │ ├── dataFactory_pipeline.jpg │ │ ├── dragtableitem.gif │ │ ├── dragtableitemx.gif │ │ ├── ellipses.jpg │ │ ├── endpoint.jpg │ │ ├── examplechartpage.jpg │ │ ├── extract.jpg │ │ ├── fabriccapacity.jpg │ │ ├── homepage_powerBI.jpg │ │ ├── import.jpg │ │ ├── insertcell.jpg │ │ ├── j561qrhd.jpg │ │ ├── keyvaultimport.jpg │ │ ├── keyvaults.jpg │ │ ├── lakehopusecontainer.jpg │ │ ├── lakehouse-bronze.jpg │ │ ├── lastonenotchecked.jpg │ │ ├── lastreport.jpg │ │ ├── maintenance.jpg │ │ ├── medallion_structure.jpg │ │ ├── model1.jpg │ │ ├── msEntraID.jpg │ │ ├── newnotebook.jpg │ │ ├── notification121.jpg │ │ ├── notifications.jpg │ │ ├── p2li0cte.jpg │ │ ├── paidfeatures60days.jpg │ │ ├── refresh3.jpg │ │ ├── removelakehouses.jpg │ │ ├── reporting45.jpg │ │ ├── roleinkeyvault.jpg │ │ ├── rport.jpg │ │ ├── runALL.jpg │ │ ├── save+run.jpg │ │ ├── secretConfirmed.jpg │ │ ├── selectDataFactory.jpg │ │ ├── selectSQLDatabase.jpg │ │ ├── selectSQLDatabase2.jpg │ │ ├── selectSQLDatabase3.jpg │ │ ├── selectSQLDatabase4.jpg │ │ ├── selectSQLDatabase5.jpg │ │ ├── selectSQLDatabase6.jpg │ │ ├── selectbronzeandcsv.jpg │ │ ├── selectimport.jpg │ │ ├── selectnotebook.jpg │ │ ├── selecttables.jpg │ │ ├── servprincsecret.jpg │ │ ├── servprincselect.jpg │ │ ├── shortcutscreated.jpg │ │ ├── sqlbutton.jpg │ │ ├── sqldatabases.jpg │ │ ├── startandattach.jpg │ │ ├── startcompute.jpg │ │ ├── startcomputeprompt.jpg │ │ ├── startrunattach.jpg │ │ ├── storageconnected.jpg │ │ ├── upgradetoPAIDpowerBI.jpg │ │ ├── upgradetrial.jpg │ │ └── workspacecreated.jpg │ ├── instructions257645 │ │ ├── 3lq6s0xq.jpg │ │ ├── 5bxsy6y0.jpg │ │ ├── Copydataassistant.png │ │ ├── Focus-mode.png │ │ ├── Insert-cell.png │ │ ├── Run-all.png │ │ ├── Run-maintenance.png │ │ ├── azure-kv-scope.png │ │ ├── fabric3.jpg │ │ ├── last-execution-time.png │ │ ├── lcp8qyu0.jpg │ │ ├── new-relationship.png │ │ ├── new2fabric.jpg │ │ ├── newfabric1.jpg │ │ ├── oqq6n4br.jpg │ │ ├── removelakehouses.jpg │ │ ├── servprincsecret1121.jpg │ │ ├── stacked-column-chart.png │ │ └── v4pfpe1k.jpg │ ├── instructions268353 │ │ ├── Copilot pane.png │ │ ├── CopilotPane.png │ │ ├── Filtered Query.png │ │ ├── FilteredQuery.png │ │ ├── Get Data.png │ │ ├── GetData.png │ │ ├── Lakehouse completed.png │ │ ├── LakehouseCompleted.png │ │ ├── New query.png │ │ ├── New_query.png │ │ ├── Power Query.png │ │ ├── PowerQuery.png │ │ ├── Run cell.png │ │ ├── Run_cell.png │ │ ├── Runall.png │ │ ├── Tables.png │ │ ├── filtered results2.png │ │ ├── filteredResults2.png │ │ ├── new cell.png │ │ ├── new code.png │ │ ├── new-relationship.png │ │ ├── new_cell.png │ │ ├── new_code.png │ │ └── submit.png │ ├── instructions270841 │ │ ├── aa22.jpg │ │ ├── oc7u1il9.jpg │ │ ├── readme.txt │ │ ├── sk5ab2xa.jpg │ │ ├── vtyg4ose.jpg │ │ └── w7edmt9i.jpg │ └── screens │ │ └── fx3uajf1.jpg └── resources │ └── BronzeToSilver.zip └── index.md /.github/workflows/pages.yml: -------------------------------------------------------------------------------- 1 | # This workflow uses actions that are not certified by GitHub. 2 | # They are provided by a third-party and are governed by 3 | # separate terms of service, privacy policy, and support 4 | # documentation. 5 | 6 | # Sample workflow for building and deploying a Jekyll site to GitHub Pages 7 | name: Deploy Jekyll site to Pages 8 | 9 | on: 10 | # Runs on pushes targeting the default branch 11 | push: 12 | branches: ["main"] 13 | 14 | # Allows you to run this workflow manually from the Actions tab 15 | workflow_dispatch: 16 | 17 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages 18 | permissions: 19 | contents: read 20 | pages: write 21 | id-token: write 22 | 23 | # Allow only one concurrent deployment to GitHub Pages 24 | concurrency: 25 | group: "pages" 26 | cancel-in-progress: true 27 | 28 | jobs: 29 | # Build job 30 | build: 31 | runs-on: ubuntu-latest 32 | steps: 33 | - name: Checkout 34 | uses: actions/checkout@v4 35 | - name: Setup Ruby 36 | uses: ruby/setup-ruby@v1 37 | with: 38 | ruby-version: '3.1' # Not needed with a .ruby-version file 39 | bundler-cache: true # runs 'bundle install' and caches installed gems automatically 40 | cache-version: 0 # Increment this number if you need to re-download cached gems 41 | - name: Setup Pages 42 | id: pages 43 | uses: actions/configure-pages@v4 44 | - name: Build with Jekyll 45 | # Outputs to the './_site' directory by default 46 | run: bundle exec jekyll build --baseurl "${{ steps.pages.outputs.base_path }}" 47 | env: 48 | JEKYLL_ENV: production 49 | - name: Upload artifact 50 | uses: actions/upload-pages-artifact@v3 51 | 52 | # Deployment job 53 | deploy: 54 | environment: 55 | name: github-pages 56 | url: ${{ steps.deployment.outputs.page_url }} 57 | runs-on: ubuntu-latest 58 | needs: build 59 | steps: 60 | - name: Deploy to GitHub Pages 61 | id: deployment 62 | uses: actions/deploy-pages@v4 63 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Microsoft Open Source Code of Conduct 2 | 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 4 | 5 | Resources: 6 | 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns 10 | -------------------------------------------------------------------------------- /Gemfile: -------------------------------------------------------------------------------- 1 | source 'https://rubygems.org' 2 | 3 | gem "jekyll", "~> 4.3" # installed by `gem jekyll` 4 | # gem "webrick" # required when using Ruby >= 3 and Jekyll <= 4.2.2 5 | 6 | gem "just-the-docs", "0.4.2" # pinned to the current release 7 | # gem "just-the-docs" # always download the latest release 8 | -------------------------------------------------------------------------------- /Gemfile.lock: -------------------------------------------------------------------------------- 1 | GEM 2 | remote: https://rubygems.org/ 3 | specs: 4 | addressable (2.8.1) 5 | public_suffix (>= 2.0.2, < 6.0) 6 | colorator (1.1.0) 7 | concurrent-ruby (1.1.10) 8 | em-websocket (0.5.3) 9 | eventmachine (>= 0.12.9) 10 | http_parser.rb (~> 0) 11 | eventmachine (1.2.7) 12 | ffi (1.15.5) 13 | forwardable-extended (2.6.0) 14 | http_parser.rb (0.8.0) 15 | i18n (1.12.0) 16 | concurrent-ruby (~> 1.0) 17 | jekyll (4.3.0) 18 | addressable (~> 2.4) 19 | colorator (~> 1.0) 20 | em-websocket (~> 0.5) 21 | i18n (~> 1.0) 22 | jekyll-sass-converter (>= 2.0, < 4.0) 23 | jekyll-watch (~> 2.0) 24 | kramdown (~> 2.3, >= 2.3.1) 25 | kramdown-parser-gfm (~> 1.0) 26 | liquid (~> 4.0) 27 | mercenary (>= 0.3.6, < 0.5) 28 | pathutil (~> 0.9) 29 | rouge (>= 3.0, < 5.0) 30 | safe_yaml (~> 1.0) 31 | terminal-table (>= 1.8, < 4.0) 32 | webrick (~> 1.7) 33 | jekyll-sass-converter (2.2.0) 34 | sassc (> 2.0.1, < 3.0) 35 | jekyll-seo-tag (2.8.0) 36 | jekyll (>= 3.8, < 5.0) 37 | jekyll-watch (2.2.1) 38 | listen (~> 3.0) 39 | just-the-docs (0.4.2) 40 | jekyll (>= 3.8.5) 41 | jekyll-seo-tag (>= 2.0) 42 | rake (>= 12.3.1) 43 | kramdown (2.4.0) 44 | rexml 45 | kramdown-parser-gfm (1.1.0) 46 | kramdown (~> 2.0) 47 | liquid (4.0.3) 48 | listen (3.7.1) 49 | rb-fsevent (~> 0.10, >= 0.10.3) 50 | rb-inotify (~> 0.9, >= 0.9.10) 51 | mercenary (0.4.0) 52 | pathutil (0.16.2) 53 | forwardable-extended (~> 2.6) 54 | public_suffix (5.0.0) 55 | rake (13.0.6) 56 | rb-fsevent (0.11.2) 57 | rb-inotify (0.10.1) 58 | ffi (~> 1.0) 59 | rexml (3.2.5) 60 | rouge (4.0.0) 61 | safe_yaml (1.0.5) 62 | sassc (2.4.0) 63 | ffi (~> 1.9) 64 | terminal-table (3.0.2) 65 | unicode-display_width (>= 1.1.1, < 3) 66 | unicode-display_width (2.3.0) 67 | webrick (1.7.0) 68 | 69 | PLATFORMS 70 | arm64-darwin-21 71 | x86_64-darwin-19 72 | x86_64-linux 73 | 74 | DEPENDENCIES 75 | jekyll (~> 4.3) 76 | just-the-docs (= 0.4.2) 77 | 78 | BUNDLED WITH 79 | 2.3.9 80 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Project 2 | 3 | > This repo has been populated by an initial template to help get you started. Please 4 | > make sure to update the content to build a great experience for community-building. 5 | 6 | As the maintainer of this project, please make a few updates: 7 | 8 | - Improving this README.MD file to provide a great experience 9 | - Updating SUPPORT.MD with content about this project's support experience 10 | - Understanding the security reporting process in SECURITY.MD 11 | - Remove this section from the README 12 | 13 | ## Contributing 14 | 15 | This project welcomes contributions and suggestions. Most contributions require you to agree to a 16 | Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us 17 | the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. 18 | 19 | When you submit a pull request, a CLA bot will automatically determine whether you need to provide 20 | a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions 21 | provided by the bot. You will only need to do this once across all repos using our CLA. 22 | 23 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 24 | For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or 25 | contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. 26 | 27 | ## Trademarks 28 | 29 | This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 30 | trademarks or logos is subject to and must follow 31 | [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). 32 | Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. 33 | Any use of third-party trademarks or logos are subject to those third-party's policies. 34 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## Security 4 | 5 | Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin). 6 | 7 | If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below. 8 | 9 | ## Reporting Security Issues 10 | 11 | **Please do not report security vulnerabilities through public GitHub issues.** 12 | 13 | Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report). 14 | 15 | If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp). 16 | 17 | You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 18 | 19 | Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: 20 | 21 | * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) 22 | * Full paths of source file(s) related to the manifestation of the issue 23 | * The location of the affected source code (tag/branch/commit or direct URL) 24 | * Any special configuration required to reproduce the issue 25 | * Step-by-step instructions to reproduce the issue 26 | * Proof-of-concept or exploit code (if possible) 27 | * Impact of the issue, including how an attacker might exploit the issue 28 | 29 | This information will help us triage your report more quickly. 30 | 31 | If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs. 32 | 33 | ## Preferred Languages 34 | 35 | We prefer all communications to be in English. 36 | 37 | ## Policy 38 | 39 | Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd). 40 | 41 | 42 | -------------------------------------------------------------------------------- /SUPPORT.md: -------------------------------------------------------------------------------- 1 | # TODO: The maintainer of this repo has not yet edited this file 2 | 3 | **REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project? 4 | 5 | - **No CSS support:** Fill out this template with information about how to file issues and get help. 6 | - **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). CSS will work with/help you to determine next steps. 7 | - **Not sure?** Fill out an intake as though the answer were "Yes". CSS will help you decide. 8 | 9 | *Then remove this first heading from this SUPPORT.MD file before publishing your repo.* 10 | 11 | # Support 12 | 13 | ## How to file issues and get help 14 | 15 | This project uses GitHub Issues to track bugs and feature requests. Please search the existing 16 | issues before filing new issues to avoid duplicates. For new issues, file your bug or 17 | feature request as a new Issue. 18 | 19 | For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE 20 | FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER 21 | CHANNEL. WHERE WILL YOU HELP PEOPLE?**. 22 | 23 | ## Microsoft Support Policy 24 | 25 | Support for this **PROJECT or PRODUCT** is limited to the resources listed above. 26 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | title: Fabric w/ Azure Databricks 2 | description: This lab showcases Modern Analytics with Microsoft Fabric and Azure Databricks, featuring a cost-effective, performance-optimized, and cloud-native Analytics solution pattern. 3 | theme: just-the-docs 4 | 5 | aux_links: 6 | "TechExcel Fabric wth Databricks for Data Analytics": 7 | - "https://github.com/microsoft/TechExcel-Fabric-with-Databricks-for-Data-Analytics" 8 | 9 | callouts_level: quiet # or loud 10 | callouts: 11 | highlight: 12 | color: yellow 13 | important: 14 | title: Important 15 | color: blue 16 | new: 17 | title: New 18 | color: green 19 | note: 20 | title: Note 21 | color: purple 22 | warning: 23 | title: Warning 24 | color: red 25 | -------------------------------------------------------------------------------- /_includes/components/breadcrumbs.html: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /docs/Ex_00/0001.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Configure a service account and secret' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 01: Provision and configure resources' 6 | --- 7 | 8 | # Task 01: Configure a service account and secret 9 | 10 | 11 | Configuring a service principal and secret for accessing a Databricks workspace ensures secure authentication and authorization for programmatic interactions. The service principal and secret separate sensitive credentials, allow fine-grained control over permissions, and enable applications to securely access Databricks resources while adhering to the principal of least privilege. 12 | 13 | In this task, you’ll create a service account and secret for an existing app registration. 14 | 15 | {: .important } 16 | > If you want to learn more about creating a service principal, go to [**Register a Microsoft Entra app and create a service principal**](https://learn.microsoft.com/en-us/entra/identity-platform/howto-create-service-principal-portal "Register a Microsoft Entra app and create a service principal"). 17 | 18 | 19 | 1. Open a browser and go to [**https://portal.azure.com/**](https://portal.azure.com/). Sign in by using the following credentials. 20 | 21 | | Setting | Value | 22 | |:---------|:---------| 23 | | Username | **Your Azure Username** | 24 | | Password | **Your Azure Password** | 25 | 26 | 1. On the Azure Home page, in the search box, search for and select **Microsoft Entra ID**. 27 | 28 | 29 | 30 | 1. On the **Microsoft Entra ID Overview** page, in the left menu, select **Manage** and then select **App registrations**. 31 | 32 | 33 | 34 | 1. On the menu, select **+ New registration**. 35 | 36 | 37 | 38 | 1. On the **Register an Application** page, enter **fabric@lab.LabInstance.Id** for the name. 39 | 40 | 41 | 42 | 1. Select **Register**. 43 | 44 | 45 | 46 | 1. On the **Overview** page, locate the **Essentials** section at the top of the page. Paste the values for the following settings into the appropriate text boxes: 47 | 48 | 49 | 50 | | Default | Value | 51 | |:---------|:---------| 52 | | Display name | **The name of your service account (e.g., MyServiceAccount)** | 53 | | Application (client) ID | **The unique identifier for your app (e.g., [Your Application (client) ID])** | 54 | | Directory (tenant) ID | **The unique identifier for your directory (e.g., [Your Directory (tenant) ID])** | 55 | 56 | 57 | 58 | {: .warning } 59 | > Be sure to move your mouse cursor outside of the text box after pasting text into any text boxes in the instructions. This ensures that the lab environment will save the values for use later in the lab. 60 | 61 | 62 | {: .note } 63 | > You may find it helpful to copy these values into Notepad as well. At times, a page refresh will reset the entered value and require re-entering the values. 64 | 65 | 66 | 1. In the left menu pane, select **Manage** and then select **Certificates & secrets**. 67 | 68 | 69 | 70 | 1. Select the **Client secrets** tab and then select **+ New client secret**. 71 | 72 | 73 | 74 | 1. In the Add a client secret window, select the **Description** box, enter **serviceAccountSecret**, and then select **Add**. 75 | 76 | 77 | 78 | ![servprincsecret1121.jpg](../media/instructions257645/servprincsecret1121.jpg) 79 | 80 | 81 | 82 | 1. On the **Certificates & secrets** page for the application, locate the secret that you just created. Copy the following values for **Description** and **Value** and paste these values into the appropriate text boxes: 83 | 84 | 85 | 86 | >{: .warning } After you save the client secret, the value of the client secret is displayed. After you navigate away from the **Certificates & secrets** page, the value of the secret will no longer be available. Save the value in Notepad or elsewhere. 87 | 88 | 89 | | Default | Value | 90 | |:---------|:---------| 91 | | Description | **@lab.TextBox(secretDescription)** | 92 | | Value | **@lab.TextBox(secretValue)** | 93 | -------------------------------------------------------------------------------- /docs/Ex_00/0002.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Create and add a client secret to an Azure Key Vault instance' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 01: Provision and configure resources' 6 | --- 7 | 8 | # Task 02: Create and add a client secret to an Azure Key Vault instance 9 | 10 | Azure Key Vault is used to securely store and manage sensitive information such as passwords, encryption keys, and certificates. Key Vault provides a centralized and highly secure storage solution, safeguarding these critical assets from unauthorized access. 11 | 12 | By using Azure Key Vault, organizations can enhance security practices by reducing the risk of exposing sensitive data within applications or configuration files. Key Vault integrates seamlessly with other Azure services, enabling easy access to stored secrets while enforcing robust access control policies and auditing capabilities for compliance purposes. 13 | 14 | In this task, you’ll provision an Azure Key Vault instance. 15 | 16 | 17 | >{: .important } If you want to learn more about Azure Key Vault, go to [**Azure Key Vault basic concepts**](https://learn.microsoft.com/en-us/azure/key-vault/general/basic-concepts "Azure Key Vault basic concepts"). 18 | 19 | 20 | 21 | 1. Return to the Azure Home page. 22 | 23 | 24 | 25 | 1. On the Azure Home page, in the search box, search for and select **Key vaults**. 26 | 27 | 28 | 29 | 1. Select **kv-@lab.LabInstance.Id**. 30 | 31 | 32 | 33 | 1. In the left menu, select **Objects** and then select **Secrets**. 34 | 35 | 36 | 37 | 1. On the menu, select **+ Generate/Import**. 38 | 39 | 40 | 41 | ![keyvaultimport.jpg](../media/instructions254096/keyvaultimport.jpg) 42 | 43 | 44 | 45 | 1. Configure the secret by using the following values: 46 | 47 | 48 | 49 | | Default | Value | 50 | |:---------|:---------| 51 | | Name | **[Your Secret Name]** (e.g., appServiceRegistrationSecret) | 52 | | Secret value | **[Your Secret Value]** | 53 | 54 | >{: .important } To access resources in your subscription, you must assign a role to the application. The process includes granting permissions by using Azure Key Vault. This ensures secure storage and retrieval of client secrets for authentication and authorization purposes. 55 | 56 | 57 | 58 | 1. Select **Create**. 59 | 60 | 61 | 62 | 1. In the left menu pane for the **Key vault secrets** page, select **Access control (IAM)**. In the **Grant access to this resource** tile, select **Add role assignment**. 63 | 64 | 65 | 66 | ![addRoleassign.jpg](../media/instructions254096/addRoleassign.jpg) 67 | 68 | 69 | 70 | 1. In the search box, enter and select **Key Vault Contributor**, and then select **Next**. 71 | 72 | {: .warning } 73 | > You must select **Key Vault Contributor** from the list of search results before you select **Next** to ensure that you add members correctly to the role. 74 | 75 | 1. On the **Members** tab, in the **Members** section, select **+ Select members**. 76 | 77 | 78 | 79 | 1. In the Select members window, enter and select the appropriate member (e.g., [Your Service Account Name]) from the list and then select **Select**. 80 | 81 | 82 | 83 | 1. Select **Review + assign** twice. 84 | 85 | -------------------------------------------------------------------------------- /docs/Ex_00/0004.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '3. Add a Lakehouse container to the Storage account.' 3 | layout: default 4 | nav_order: 3 5 | parent: 'Exercise 01: Provision and configure resources' 6 | --- 7 | 8 | # Task 03: Add a Lakehouse container to the Storage account. 9 | 10 | 11 | As part of the pre-deployment steps for this lab environment, a storage account named storage@lab.LabInstance.Id. The account will serve as an Azure Data Lake Storage Gen2 (ADLS Gen2) storage account to provide a scalable, secure, and cost-effective storage for big data analytics workloads. 12 | 13 | Azure Data Lake Storage Gen2 (ADLS Gen2) storage accounts combine the capabilities of Azure Blob Storage with the hierarchical namespace feature of Azure Data Lake Storage Gen1 to offer enhanced performance, security, and analytics capabilities. 14 | 15 | In this task, you will add a role assignment to the storage account. You will then add a container named medallion and subfolders to contain the bronze, silver, and gold medallion data. 16 | 17 | {: .highlight } 18 | > If you want to learn more about Azure SQL Database, go to [**Introduction to Azure Data Lake Storage Gen2**](https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction "Introduction to Azure Data Lake Storage Gen2"). 19 | 20 | 1. Return to the Azure Home page. 21 | 22 | 1. On the Azure search box, enter and select **Storage accounts**. 23 | 24 | 1. Select the storage account you wish to use for this task. 25 | 26 | 1. In the left navigation pane, select **Access Control (IAM)**. 27 | 28 | 1. In the left menu pane for the **Storage account** page, select **Access control (IAM)**. 29 | 30 | 1. In the **Grant access to this resource** tile, select **Add role assignment**. 31 | 32 | 1. In the Search field, search for **Storage Blob Data Contributor**. 33 | 34 | 1. Select **Storage Blob Data Contributor** from the search results and then select **Next**. 35 | 36 | {: .note } 37 | > You must select **Storage Blob Data Contributor** from the list of search results before you select **Next** to ensure that you add members correctly to the role. 38 | 39 | 1. On the Members tab, in the Members section, select **+ Select members**. 40 | 41 | {: .note } 42 | > You’ll add two members for this role assignment. 43 | 44 | 1. In the **Select members** search box, enter the username of the first user you want to assign this role to and then select the user from the search results. Select **Select**. 45 | 46 | 1. On the **Add role assignment** page, select **+ Select members** to add the second member. 47 | 48 | 1. In the **Select members** search box, enter the service account name (or another member) that you want to assign this role to, and then select the member from the search results. Select **Select**. 49 | 50 | 1. On the Add role assginment page, select **Review + assign** twice. 51 | 52 | 1. On the **Access Control (IAM)** page for the storage account, in the left navigation pane, select **Data storage** and then select **Containers**. 53 | 54 | 1. On the menu for the Storage account **Containers** page, select **medallion**. 55 | 56 | 1. Verify that the **bronze**, **silver**, and **gold** directories exist. 57 | 58 | ![medallion_structure.jpg](../media/instructions254096/medallion_structure.jpg) 59 | -------------------------------------------------------------------------------- /docs/Ex_00/Ex00.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 01: Provision and configure resources' 3 | layout: default 4 | nav_order: 2 5 | has_children: true 6 | --- 7 | 8 | # Exercise 01: Provision and configure resources 9 | 10 | In this exercise, you’ll prepare the lab environment by performing the following tasks: 11 | 12 | - Configure an account service principal and secret. 13 | 14 | - Add the client secret. 15 | -------------------------------------------------------------------------------- /docs/Ex_01/0101.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Create a Microsoft Fabric-enabled workspace' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 02: Create a Fabric workspace and data pipeline' 6 | --- 7 | 8 | # Task 01: Create a Microsoft Fabric-enabled workspace 9 | 10 | 1. Open a new browser tab and go to **[https://app.powerbi.com/](https://app.powerbi.com/)** 11 | 12 | {: .note } 13 | > You may see a message alerting you that you have been assigned a Microsoft Fabric (Free) license. Select **OK** to dismiss the message. 14 | 15 | 1. On the **You've selected Microsoft Fabric free** page, select **Continue** if prompted. If necessary, enter any random 10 digits or use `5551234567` and select **Get Started** twice. 16 | 17 | 1. On the Power BI Home page, in the left navigation pane, select **Workspaces** and then select **+ New workspace**. 18 | 19 | 1. In the **Name** box, enter a unique name for your workspace (e.g., ContosoSales). After Azure verifies that the name is available,select **Advanced**. 20 | 21 | 1. In the **License mode** section, verify that **Fabric capacity** is selected and then select **Apply**. 22 | 23 | ![fabriccapacity.jpg](../media/instructions254096/fabriccapacity.jpg) 24 | 25 | 1. On the Power BI **[Your Workspace Name]** page, select **Workspaces** and verify that the workspace you created is listed. 26 | 27 | ![workspacecreated.jpg](../media/instructions254096/workspacecreated.jpg) 28 | 29 | {: .note } 30 | > If your workspace was not created, repeat the steps in this task to create the workspace. 31 | 32 | 1. Select the workspace you just created to access it. 33 | -------------------------------------------------------------------------------- /docs/Ex_01/0102.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Create Lakehouses' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 02: Create a Fabric workspace and data pipeline' 6 | --- 7 | 8 | # Task 02: Create Lakehouses 9 | 10 | Now, let's see how each department in Contoso can easily create a Lakehouse in their workspace without any provisioning by simply providing the name, given the proper access rights, of course! 11 | 12 | 1. In the left menu pane for the page, select **ContosoSales@lab.LabInstance.Id**. On the **ContosoSales@lab.LabInstance.Id** page, select **+ New item**. 13 | 14 | 1. Scroll down to the **Store data** section of the New item dialog and select **Lakehouse**. 15 | 16 | 1. In the **Name** field for the New lakhouse dialog, enter +++**bronze**+++ and then select **Create**. 17 | 18 | !MAGE[bronzelakehouse.jpg](../media/instructions254096/bronzelakehouse.jpg) 19 | 20 | 1. Repeat steps 1-3 to create two more Lakehouses. Use the following names for the new Lakehouses: 21 | 22 | | Lakehouse Name | 23 | |:---------| 24 | | **silver** | 25 | |**gold** | 26 | 27 | 1. In the left navigation pane for the Synapse Data Engineering Home page, select the name of your workspace. You should see the three Lakehouses and related objects listed. 28 | 29 | ![Lakehouse completed.png](../media/instructions268353/LakehouseCompleted.png) 30 | 31 | {: .important } 32 | > Now you are ready to start data ingestion. You will first ingest the raw data in the bronze layer. After that, you will curate and enrich the data as you move the data to the silver and then gold layer. -------------------------------------------------------------------------------- /docs/Ex_01/0103.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '3. Using data pipelines/data flow for data ingestion' 3 | layout: default 4 | nav_order: 3 5 | parent: 'Exercise 02: Create a Fabric workspace and data pipeline' 6 | --- 7 | 8 | # Task 03: Using data pipelines/data flow for data ingestion 9 | 10 | There are multiple ways to ingest data into a Lakehouse, and in this exercise, Contoso focuses on using data pipelines and data flow to efficiently funnel diverse datasets into their system, setting the stage for advanced analytics and insights. 11 | 12 | 1. On the **ContosoSales@lab.LabInstance.Id** page, select **+ New item**. 13 | 14 | 1. Scroll to the **Get data** section of the New item dialog and select **Data pipeline**. 15 | 16 | 1. In the dialog that displays, enter **Azure SQL DB Pipeline** for the pipeline name and then select **Create**. 17 | 18 | 1. Select the **Copy data assistant** tile. 19 | 20 | ![Copydataassistant.png](../media/instructions257645/Copydataassistant.png) 21 | 22 | 23 | 24 | 1. On the **Choose data source** page, select **Azure SQL Database**. You may need to scroll down to see the **Azure SQL Database** option. 25 | 26 | ![selectSQLDatabase.jpg](../media/instructions254096/selectSQLDatabase.jpg) 27 | 28 | 1. Configure the connection by using the values in the following table. Leave all other settings at their default values. 29 | 30 | {: .warning } 31 | > If there’s no value listed for the **Server** setting, right-click the instructions pane in the lab environment and select **Refresh**. 32 | 33 | | Default | Value | 34 | |:---------|:---------| 35 | | Server | **sql@lab.LabInstance.Id.database.windows.net** | 36 | | Database | **DB01** | 37 | | Authentication kind | **Basic** | 38 | | Username | **azureadmin1** | 39 | | Password | **@lab.CloudPortalCredential(User1).Password** | 40 | 41 | ![selectSQLDatabase2.jpg](../media/instructions254096/selectSQLDatabase2.jpg) 42 | 43 | 1. Select **Next**. Close any pop-up windows that display and wait for the connection to be created. 44 | 45 | 1. On the **Connect to data source** page, select **Tables**. 46 | 47 | {: .note } 48 | > It may take 30-60 seconds to display the list of tables. 49 | 50 | 1. Select **Select all**, clear the **dbo.BuildVersion** and **dbo.ErrorLog** checkboxes, and then select **Next**. 51 | 52 | ![selecttables.jpg](../media/instructions254096/selecttables.jpg) 53 | 54 | 1. On the **Choose data destinations** page, search for and select +++Azure Data Lake Storage Gen2+++. 55 | 56 | ![adlsgen2.jpg](../media/instructions254096/adlsgen2.jpg) 57 | 58 | 1. On the **Connect to data destination** page, enter the following to create a new connection: 59 | 60 | | Default | Value | 61 | |:---------|:---------| 62 | | URL | **https://sa@lab.LabInstance.Id.dfs.core.windows.net/** | 63 | | Authentication kind | **Organizational account** | 64 | 65 | 66 | 69 | 70 | {: .important } 71 | > The connection URL for the Data Lake Storage account can be located here: Storage account > Settings > Endpoints > Data Lake Storage. 72 | 73 | ![endpoint.jpg](../media/instructions254096/endpoint.jpg) 74 | 75 | 1. Select the account that’s already authenticated and then select **Next**. 76 | 77 | 1. On the **Connect to data destination** page, next to the **Folder path** box, select **Browse**. 78 | 79 | 1. Select **medallion** > **bronze** and then select **OK**. 80 | 81 | 1. In the **File name suffix** box, enter **.csv** and then select **Next** to test the connection. 82 | 83 | ![selectbronzeandcsv.jpg](../media/instructions254096/selectbronzeandcsv.jpg) 84 | 85 | 1. Select **Next** and then select **Save + Run**. After a brief delay, the Pipeline Run window displays. 86 | 87 | ![save+run.jpg](../media/instructions254096/save+run.jpg) 88 | 89 | 1. In the Pipeline Run window, select **OK**. The pipeline will start processing. 90 | 91 | 1. On the upper right of the page, select **Notifications**. You can use the Notifications area to monitor the pipeline. 92 | 93 | ![notifications.jpg](../media/instructions254096/notifications.jpg) 94 | 95 | 1. At the lower left of the page, above **Data Factory**, select the ellipses (**...**) icon and then select **Monitor**. 96 | 97 | 1. Verify that the value in the **Status** box for the pipeline is **Succeeded**. 98 | 99 | {: .warning } 100 | > Please wait for the pipeline to execute. If the notification continues to say it’s running after 10 minutes, check the monitoring hub for a succeeded status. 101 | > 102 | >![0faouzwm.png](../media/instructions249094/0faouzwm.png) 103 | 104 | 1. After the status shows **Succeeded**, your data has been transferred from Azure SQL Database to ADLS Gen 2. 105 | 106 | ![sk5ab2xa.jpg](../media/instructions270841/sk5ab2xa.jpg) -------------------------------------------------------------------------------- /docs/Ex_01/Ex01_getting_started.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 02: Create a Fabric workspace and data pipeline' 3 | layout: default 4 | nav_order: 3 5 | has_children: true 6 | --- 7 | 8 | # Exercise 02: Create a Fabric workspace and data pipeline 9 | 10 | {: .note } 11 | > Microsoft Fabric is rapidly evolving. Microsoft is updating the user interface from persona-based options to job types. In particular, there have been many recent changes to navigation and steps required to create resources. 12 | > 13 | > We review lab instructions on a regular basis and update the instructions as necessary. Please contact your coach if you're having difficulties completing any steps in this lab. 14 | 15 | Creating a Fabric workspace and data pipeline is the foundational step for consolidating Contoso's and Litware Inc.'s data, enabling unified data management and streamlined analytics within the Azure ecosystem. This infrastructure lays the groundwork for efficient data ingestion, storage, and processing. It helps the organization's address data silos and fosters a cohesive data environment. 16 | 17 | In this exercise, you will continue preparinge the lab environment by performing the following tasks: 18 | 19 | - Create a Microsoft Fabric-enabled workspace: You’ll set up a workspace that uses Microsoft Fabric, enabling integration and management of data across various sources and platforms. 20 | 21 | - Create data lakes: You’ll configure Azure Data Lake Storage instances as central repositories for big data storage and analytics, facilitating scalable data storage solutions. 22 | 23 | - Use data pipelines/data flow for data ingestion: You’ll implement data pipelines and flows to efficiently ingest data into the system, ensuring a seamless flow of information for processing and analysis. 24 | 25 | 26 | 27 | Contoso is setting up a Fabric-enabled workspace as the cornerstone for Contoso's data integration efforts, facilitating a seamless blend of disparate data sources. 28 | 29 | ![p2li0cte.jpg](../media/instructions254096/p2li0cte.jpg) 30 | -------------------------------------------------------------------------------- /docs/Ex_02/0201.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Explore Copilot for data flows' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 03: Explore Copilot capabilities' 6 | --- 7 | 8 | # Task 01: Explore Copilot for data flows 9 | 10 | 11 | 1. Open a new browser tab and go to [https://app.powerbi.com/](https://app.powerbi.com/). 12 | 13 | 1. On the **Power BI** Home page, in the left menu pane, select **Workspaces** and then select **+ New workspace**. 14 | 15 | 1. In the **Name** box, enter **CopilotDataFlowDemo@lab.LabInstance.Id**. After Azure verifies that the name is available, select **Advanced**. 16 | 17 | 1. In the **License mode** section, verify that **Fabric capacity** is selected and then select **Apply**. 18 | 19 | 1. On the **ContosoDataFlowDemo@lab.LabInstance.Id** page, select **+ New item**. 20 | 21 | 1. Scroll to the **Store data** section of the New item dialog and select **Lakehouse**. 22 | 23 | 1. In the **Name** field for the New lakehouse dialog, enter +++CopilotDataFlow@lab.LabInstance.Id+++ and then select **Create**. 24 | 25 | 1. In the left menu pane for the page, select **CopilotDataFlow@lab.LabInstance.Id** and then select **+ New item**. 26 | 27 | 1. Scroll to the Prepare data section and select **Dataflow Gen2** and then select **Create**. The collapsed Power Query ribbon displays. 28 | 29 | ![vtyg4ose.jpg](../media/instructions270841/vtyg4ose.jpg) 30 | 31 | 1. Select the down arrow (**∨**) that appears at the right end of the ribbon to expand the ribbon. 32 | 33 | ![oc7u1il9.jpg](../media/instructions270841/oc7u1il9.jpg) 34 | 35 | 1. On the **Home** tab of the Power Query ribbon, select **Copilot**. The **Copilot** pane displays on the right side of the page. 36 | 37 | ![w7edmt9i.jpg](../media/instructions270841/w7edmt9i.jpg) 38 | 39 | 1. In the **Copilot** pane, select **Get data from**. 40 | 41 | 1. On the **Choose data source** page, select **Azure SQL database**. 42 | 43 | {: .note } 44 | > For this task, you’ll connect to the same database that you connected to in the previous exercise. 45 | 46 | 1. Configure the connection by using the values in the following table. Leave all other settings at their default values. 47 | 48 | {: .warning } 49 | > If there’s no value listed for the **Server** setting, right-click the instructions pane in the lab environment and select **Refresh**. 50 | 51 | | Default | Value | 52 | |:---------|:---------| 53 | | Server | **[Your SQL Server Name]** | 54 | | Database | ****Adventureworks**** | 55 | | Authentication kind | **Basic** | 56 | | Username | **[Your SQL Admin Username]** | 57 | | Password | **[Your SQL Admin Password]** | 58 | 59 | 1. Select **Next** to test the connection. If the connection is successful, the **Choose data** page displays. 60 | 61 | 1. Select the following eight tables from the list of tables and then select **Create**. 62 | 63 | - SalesLT.Address 64 | - SalesLT.Customer 65 | - SalesLT.CustomerAddress 66 | - SalesLT.Product 67 | - SalesLT.ProductCategory 68 | - SalesLT.ProductModel 69 | - SalesLT.SalesOrderDetail 70 | - SalesLT.SalesOrderHeader 71 | 72 | ![Tables.png](../media/instructions268353/Tables.png) 73 | 74 | 1. In the **Copilot** pane, enter **Only keep Canadian customers** in the **Describe what you'd like to do** box and then select **Submit**. 75 | 76 | 1. Verify that Copilot has added a step to the query. You should see the added filter in the **Query settings** pane. You should also see the response from Copilot in the **Copilot** pane. 77 | 78 | ![aa11.jpg](../media/instructions270841/aa11.jpg) 79 | 80 | {: .warning } 81 | > Copilot for Fabric notebooks is in preview. During lab testing, we experienced mixed results when we submitted this prompt. In most cases, Coplilot successfully added a step to filter rows according to the query. Occasionally, Copilot was not able to perform the requested action. 82 | 83 | 1. If Copilot doesn’t add the query step, you can add the step manually. In the grid that previews the data, select the drop-down list next to the **CountryRegion** column. Clear the **Select All** checkbox and then select **Canada**. Select **OK** to implement the filter.

At the top of the Power Query window, you should see the formula displayed that filters the data. 84 | 85 | ![filtered results2.png](../media/instructions268353/filteredResults2.png) 86 | 87 | 1. In the **Copilot** pane, enter **Create a new query with data for official public holidays in Canada in 2024** in the **Describe what you'd like to do** box and then select **Submit**. A new query is added. 88 | 89 | ![New query.png](../media/instructions268353/New_query.png) 90 | 91 | 1. Select **Publish**. You’ll be returned to the **CopilotDataFlowDemo** workspace page. -------------------------------------------------------------------------------- /docs/Ex_02/0202.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Explore Copilot cababilites in Fabric notebooks' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 03: Explore Copilot capabilities' 6 | --- 7 | 8 | # Task 02: Explore Copilot cababilites in Fabric notebooks 9 | 10 | In this task, you will create a new workspace and lakehouse. Then, you will import the New York City (NYC) Yellow Taxicab open dataset and perform operations on the data. Finally, you will use Copilot features to ask questions about the data and generate code. 11 | 12 | 1. Open a new browser tab and go to **[https://app.powerbi.com/](https://app.powerbi.com/)**. 13 | 14 | 1. On the Power BI Home page, in the left navigation pane, select **Workspaces** and then select **+ New workspace**. 15 | 16 | 1. In the **Name** box, enter enter a unique name for your workspace **(e.g., CopilotInFabricDemo).** After Azure verifies that the name is available, select **Advanced**. 17 | 18 | 1. In the **License mode** section, verify that **Fabric capacity** is selected and then select **Apply**. 19 | 20 | 1. On your **CopilotDataFlowDemo** workspace page, select **+ New item**. 21 | 22 | 1. In the **Store data** section of the New item dialog, select **Lakehouse**. 23 | 24 | 1. In the **Name** field for the New lakhouse dialog, 25 | 26 | 27 | 28 | 1. On the menu for the CopilotInFabricDemo lakehouse, select **Open notebook** > **New notebook**. 29 | 30 | 1. At the upper left of the page, select the notebook name. Replace the name with **CopilotDemoNotebook** and select **Enter**. 31 | 32 | 1. On the menu for the notebook, select **Copilot**. 33 | 34 | {: .note } 35 | > You may need to select the ellipses (**...**) to see the **Copilot** option. 36 | 37 | 1. Select **Get Started**. Copilot adds a new cell to the notebook. 38 | 39 | 1. Select the **Run cell** button to install the packages that Copilot needs. 40 | 41 | ![Run cell.png](../media/instructions268353/Run_cell.png) 42 | 43 | {: .note } 44 | > Copilot adds a new cell above the current cell. It may take 1-3 minutes for the installation process to complete. 45 | 46 | 1. Move the cursor to the lower left of the last cell in the notebook and select **+ code** to add a new cell. 47 | 48 | ![new cell.png](../media/instructions268353/new_cell.png) 49 | 50 | 1. Enter the following code in the new cell and then select **Run cell**. 51 | 52 | {: .note } 53 | > This code specifies Azure storage access and connectivity information for the NYC Yellow Taxi open dataset. The last line of code filters the data to limit the volume of data that you’ll ingest for this exercise. 54 | 55 | 56 | ``` 57 | storage_account_name = "azureopendatastorage" 58 | container_name = "nyctlc" 59 | 60 | sas_token = r"" # Specify blank since container is public with anonymous access 61 | 62 | spark.conf.set("fs.azure.sas.%s.%s.blob.core.windows.net" % (container_name, storage_account_name),sas_token) 63 | 64 | directory = "yellow" 65 | year = 2016 66 | months = "1,2,3,4,5,6" 67 | wasbs_path = f"wasbs://{container_name}@{storage_account_name}.blob.core.windows.net/{directory}" 68 | nyc_yellowtaxi_df = spark.read.parquet(wasbs_path) 69 | 70 | filtered_nyc_yellowtaxi_df = nyc_yellowtaxi_df.filter(f"puYear = {year} AND puMonth IN ({months})") 71 | ``` 72 | 73 | {: .warning } 74 | > As each cell runs, you will see a message stating that Spark jobs are in progress. When processing completes you will see a messange stating that Spark jobs succeeded. If the code in a specific cell fails, processing for other cells will not run. 75 | 76 | 1. Add another cell to the notebook. Add the following code to the new cell and then select **Run cell** button. This code saves the data as a delta table in the Lakehouse. 77 | 78 | ``` 79 | table_name = "nyc_yellowtaxi_raw" 80 | 81 | filtered_nyc_yellowtaxi_df.write.mode("overwrite").format("delta").saveAsTable(f"{table_name}") 82 | print(f"Spark dataframe (filtered_nyc_yellowtaxi_df) saved to a delta table: {table_name}") 83 | ``` 84 | {: .note } 85 | > Cell execution could take up to 2 minutes to complete. 86 | 87 | 1. Add another cell to the notebook. Add the following code to the new cell and then select **Run cell** button. This code runs a query to select and aggregate data. 88 | 89 | ``` 90 | %%sql 91 | select puYear, puMonth, count(*) from nyc_yellowtaxi_raw group by puYear, puMonth order by puYear, puMonth 92 | ``` 93 | 94 | {: .note } 95 | > You should see six rows in the ouput dataset from the query. Each row includes the year, month, and the number of records for that period. 96 | 97 | 1. Add another cell to the notebook. Add the following code to the new cell and then select **Run cell** button. This code counts the number of records returned. 98 | 99 | ``` 100 | filtered_nyc_yellowtaxi_df.count() 101 | ``` 102 | 103 | {: .note } 104 | > During testing, 69,402,938 rows were returned. 105 | 106 | 107 | 1. At the bottom of the Copilot pane, enter the following prompt and then press the Enter key: 108 | 109 | ``` 110 | Describe the structure of the filtered_nyc_yellowtaxi_df dataframe 111 | ``` 112 | 113 | {: .warning } 114 | > Copilot for Fabfric notebooks is in preview. During lab testing, we experienced mixed results when we submitted this prompt. In some cases, Coplilot responds with a Python command that you can enter in a cell to describe the dataframe structure. The command should resememble the following: 115 | > 116 | > filtered_nyc_yellowtaxi_df.describe().show()

In other cases, Copilot responded with "I'm unable to provide a description without more context or the structure of the dataset." or "I must decline to assist with that request."

These issues should be resolved as this Copilot evolves. 117 | 118 | 1. If Copilot does not create a command for you, add a new cell to the notebook. Then, add the following code to the new cell and then select **Run cell** button. 119 | 120 | ``` 121 | filtered_nyc_yellowtaxi_df.describe().show() 122 | ``` 123 | 124 | 1. At the bottom of the Copilot pane, enter the following prompt and then press the Enter key. Copilot should respond with a command that you can run to create the dataframe. 125 | 126 | ``` 127 | Create a dataframe by loading data from nyc_yellowtaxi_raw table and sampling it with 1 percentage, count the rows in the dataframe and show the amount. 128 | ``` 129 | 130 | 1. If Copilot does not create the command for you, add a new cell to the notebook. Then, add the following code to the new cell and then select **Run cell** button. 131 | 132 | ``` 133 | %%code 134 | Create a dataframe by loading data from nyc_yellowtaxi_raw table and sampling it with 1 percentage, count the rows in the dataframe and show the amount. 135 | ``` 136 | 137 | {: .note } 138 | > Running this code adds a new cell to the notebook. The new cell will contain code to create a dataframe.Try running the code. In many cases the code runs without error. You may need to make some changes to the code to get the code to run. 139 | 140 | {: .note } 141 | > This code uses the Chat-magics Python library to enhance data science and data engineering workflows in Microsoft Fabric Notebooks. The Chat-magics feature in Fabric is in preview and is subject to change. 142 | 143 | {: .important } 144 | > If you want to learn more about Chat-Magics, go to [**Overview of chat-magics in Microsoft Fabric notebook**](https://learn.microsoft.com/en-us/fabric/get-started/copilot-notebooks-chat-magics "Overview of chat-magics in Microsoft Fabric notebook"). 145 | -------------------------------------------------------------------------------- /docs/Ex_02/Ex02_create_workspace.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 03: Explore Copilot capabilities' 3 | layout: default 4 | nav_order: 4 5 | has_children: true 6 | --- 7 | 8 | # Exercise 03: Explore Copilot capabilities 9 | 10 | Microsoft has integrated Copilot and other generative AI features into Fabric to introduce new ways for yo to transform and analyze data, generate insights, and create visualizations and reports. You must enable Copilot before you can use it. Copilot in Fabric is not available in all regions. Microsoft includes the following Copilot integrations: 11 | 12 | - Data Factory: This integration includes tools that help you streamline workflows. The toolset can both generate and explain code for transforming data. 13 | - Power BI: This integration includes generative AI to build reports automatically based on topics that you select or prompts that you create. 14 | 15 | In this exercise, you will explore Copilot Data Factory capabilities. 16 | 17 | You will explore the Power BI Copilot in a later excercise in this lab. 18 | 19 | >{: .warning } 20 | >This exercise allows you to explore AI capabilities in Fabric. Your results may differ from the results that our testing team obtained. This exercise is separate from the main lab content.

You will create a separate workspace for this exercise to keep these exploratory objects separate from the main lab content. 21 | 22 | >{: .important } 23 | >If you want to learn more about Copilot in Fabric, go to [**Overview of Copilot Fabric**](https://learn.microsoft.com/en-us/fabric/get-started/copilot-fabric-overview "Overview of Copilot in Fabric"). 24 | 25 | -------------------------------------------------------------------------------- /docs/Ex_03/0301.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Create Azure Key Vault-backed secret scope for workspace' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 04: Establish connectivity between Azure Databricks and Azure Data Lake Storage (ADLS) Gen 2' 6 | --- 7 | 8 | # Task 01: Create Azure Key Vault-backed secret scope for workspace 9 | 10 | To reference the client secret stored in an Azure Key Vault, you can create a secret scope backed by Azure Key Vault in Azure Databricks. 11 | 12 | 1. Return to the browser tab that is signed into the Azure portal, go to the Home page. 13 | 14 | 1. Go to **Resource groups** > **RG1** and select the **Azure Databricks Service** resource named, **dbkws@lab.LabInstance.Id**. 15 | 16 | 1. On the **Azure Databricks Service** page, in the **Essentials** section, copy the **URL** here: `@lab.TextBox(DBURL)` 17 | 18 | 1. Select **Launch Workspace** to authenticate your Azure credentials to the workspace. 19 | 20 | {: .note } 21 | > Now that you have authenticated, you will create a secret scope for the workspace for secure access to the workspace. 22 | 23 | 1. Open a new browser tab and go to **@lab.Variable(DBURL)#secrets/createScope**. 24 | 25 | {: .important } 26 | > This URL is case sensitive; scope in createScope must be uppercase. 27 | 28 | 1. Select **Sign in with Microsoft Entra ID** if prompted. 29 | 30 | 1. On the Create Secret Scope page, enter the following informatoin and then select **Create**. 31 | 32 | | Default | Value | 33 | |:---------|:---------| 34 | | Scope Name | **scopesecret** | 35 | | Managing Principal | **All workspace Users** | 36 | | | **Azure Key Vault settings** | 37 | | DNS Name | **[Your Azure Key Vault DNS Name, e.g., https://your-keyvault-name.vault.azure.net/]** | 38 | | Resource ID | **[Your Azure Key Vault Resource ID]** | 39 | 40 | ![azure-kv-scope.png](../media/instructions254096/azure-kv-scope.png) 41 | 42 | {: .important } 43 | > These properties are available from the **Properties** tab of an Azure Key Vault in your Azure portal. 44 | > ![azure-kv.png](../media/instructions254096/azure-kv.png) 45 | 46 | 1. Select **OK**. 47 | 48 | ![secretConfirmed.jpg](../media/instructions254096/secretConfirmed.jpg) 49 | -------------------------------------------------------------------------------- /docs/Ex_03/0302.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Connect to Azure Data Lake Storage' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 04: Establish connectivity between Azure Databricks and Azure Data Lake Storage (ADLS) Gen 2' 6 | --- 7 | 8 | # Task 02: Connect to Azure Data Lake Storage 9 | 10 | Explore an analytics pipeline using open Delta format and Azure Databricks Delta Live Tables. Stitch data (landed earlier) to create a combined data product to build a simple Lakehouse and integrate with OneLake. 11 | 12 | Contoso already had some of their compute workload on **Azure Databricks**. You don’t need to migrate any of that workload to work with Fabric. You can simply use the OneLake endpoint to mount the storage and work with the same data directly from the Lakehouse for the analytical and ML operations. 13 | 14 | 1. In the left menu pane, select **Workspace**. 15 | 16 | 1. In the **Workspace > Users** pane, select the **@lab.CloudPortalCredential(User1).Username**. 17 | 18 | 1. Select **Create** and then select **Notebook**. 19 | 20 | ![selectnotebook.jpg](../media/instructions254096/selectnotebook.jpg) 21 | 22 | 1. Select the title box at the top left of the notebook. Clear the existing title and enter **Connect Databricks to ADLS**. Select **Enter** to accept the change. 23 | 24 | 1. In the first code cell, enter the following code. This code lists the available secret scopes: 25 | 26 | ``` 27 | dbutils.secrets.listScopes() 28 | ``` 29 | 30 | 1. Move the mouse cursor to the bottom left of the first code cell and then select **+ Code** to insert a new cell. 31 | 32 | ![insertcell.jpg](../media/instructions257645/Insert-cell.png) 33 | 34 | 1. In the second code cell, enter the following code. This code lists the active scopes: 35 | 36 | ``` 37 | dbutils.secrets.list("scopesecret") 38 | ``` 39 | 40 | 1. Add another cell to the notebook. Add the following code to the new cell. This code connects the Databricks workspace to the storage account by using a service principal: 41 | 42 | ``` 43 | scope="scopesecret" 44 | key="appServiceRegistrationSecret" 45 | storage_account="storage@lab.LabInstance.Id" 46 | application_id="@lab.Variable(serviceApplicationID_clientID)" 47 | directory_id="@lab.Variable(serviceDirectoryID_tenantID)" 48 | service_credential = dbutils.secrets.get(scope=scope,key=key) 49 | 50 | spark.conf.set("fs.azure.account.auth.type.%s.dfs.core.windows.net"%(storage_account), "OAuth") 51 | spark.conf.set("fs.azure.account.oauth.provider.type.%s.dfs.core.windows.net"%(storage_account), "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider") 52 | spark.conf.set("fs.azure.account.oauth2.client.id.%s.dfs.core.windows.net"%(storage_account), application_id) 53 | spark.conf.set("fs.azure.account.oauth2.client.secret.%s.dfs.core.windows.net"%(storage_account), service_credential) 54 | spark.conf.set("fs.azure.account.oauth2.client.endpoint.%s.dfs.core.windows.net"%(storage_account), "https://login.microsoftonline.com/%s/oauth2/token"%(directory_id)) 55 | ``` 56 | 57 | {: .warning } 58 | > Remember that the Type Text feature streams content into the virtual machine. Wait until the entire command is entered into the cell before you move the mouse cursor. 59 | 60 | 1. Add another cell to the notebook. Add the following code to the new cell. This code verifies the connection to the storage account. 61 | 62 | ``` 63 | container_name = "medallion" 64 | dbutils.fs.ls("abfss://%s@%s.dfs.core.windows.net/"%(container_name,storage_account)) 65 | ``` 66 | 1. In the upper-right corner, select **Run all**. 67 | 68 | ![Run-all.png](../media/instructions257645/Run-all.png) 69 | 70 | 1. Select **Allow** on the notification pop-up window. 71 | 72 | 73 | 1. In the **Attach to an existing compute resource** window, select **Start, attach and run**. 74 | 75 | ![startandattach.jpg](../media/instructions254096/startandattach.jpg) 76 | 77 | {: .warning } 78 | > It will take 3-5 minutes for the processing to complete. 79 | 80 | {: .note } 81 | > You can check the last cell of notebook for the last execution time to verify. 82 | > ![last-execution-time.png](../media/instructions257645/last-execution-time.png) 83 | -------------------------------------------------------------------------------- /docs/Ex_03/Ex03_establish_connectivity.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 04: Establish connectivity between Azure Databricks and Azure Data Lake Storage (ADLS) Gen 2' 3 | layout: default 4 | nav_order: 5 5 | has_children: true 6 | --- 7 | 8 | # Exercise 04: Establish connectivity between Azure Databricks and Azure Data Lake Storage (ADLS) Gen 2 9 | 10 | Now that you have explored some capabilities of Copilot in Fabrics, lete's get back to the main lab.By ensuring seamless data flow between Databricks and ADLS Gen 2, Contoso aims to accelerate their analytics and machine learning projects, unlocking new possibilities for innovation and efficiency. 11 | 12 | To achieve this, we’ll take the following steps: 13 | 14 | - Deploy Databricks workspace: Set up a new Azure Databricks workspace to serve as the central hub for data analytics and processing. 15 | 16 | - Authenticate Azure Databricks to the storage account using a service principal: Securely connect your Databricks workspace to the ADLS Gen 2 storage account, employing a service principal for authentication to maintain security and manageability. 17 | 18 | - Connect to the data lake: Finalize the integration by connecting Azure Databricks directly to ADLS Gen 2, enabling direct access to stored data for advanced analytics and processing. 19 | -------------------------------------------------------------------------------- /docs/Ex_04/0401.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Read from Bronze layer, convert the data into delta format and write to the Silver layer' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 05: Implement the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers)' 6 | --- 7 | 8 | # Task 01: Read from Bronze layer, convert the data into delta format and write to the Silver layer 9 | 10 | With this move we aim to clean and refine data within the Bronze layer, improving data quality and preparing it for further processing, aligning with Contoso's focus on reliable data foundations. 11 | 12 | {: .note } 13 | > Data will be written as external tables. 14 | 15 | 1. On your local machine, open **File Explorer** and go to the **Downloads** folder. 16 | 17 | 1. Right-click the **BronzeToSilver.zip** compressed folder and select **Extract All...**. 18 | 19 | ![extract.jpg](../media/instructions254096/extract.jpg) 20 | 21 | 1. Select **Extract** and then close the File Explorer window. 22 | 23 | 1. Return to the Databricks Workspace browser tab. 24 | 25 | 1. In the left navigation pane, select **Workspace**. This action causes the page to refresh. 26 | 27 | 1. In the **Workspace** pane, select **Workspace** and then select **Users**. Select **@lab.CloudPortalCredential(User1).Username**. 28 | 29 | 1. In the upper-right pane, select the vertical ellipses and then select **Import**. 30 | 31 | ![import.jpg](../media/instructions254096/import.jpg) 32 | 33 | 1. In the Import window, next to **Import from** select **File** and then select **browse**. 34 | 35 | 1. In File Explorer, go to **Downloads** and then double-click the **BronzeToSilver** folder. 36 | 37 | 1. Select the **Bronze to Silver.dbc** and **Silver to Gold.dbc** files, then select **Open**. 38 | 39 | ![selectimport.jpg](../media/instructions254096/selectimport.jpg) 40 | 41 | 1. Select **Import**. 42 | 43 | 1. After the notebooks are imported, select the **Bronze to Silver** notebook. 44 | 45 | {: .important } 46 | > We’re now getting ready to transform and store processed data in the Silver layer in delta format, enhancing Contoso's data storage efficiency and accessibility for analytical processes. 47 | 48 | 1. In the first code cell, replace the **ENTERHERE** placeholders with the following information: 49 | 50 | | Name | Value | Comment| 51 | |:---------|:---------|:---------| 52 | | scope | **scopesecret** || 53 | | key | **appServiceRegistrationSecret** |You can also retrieve the secret from the Keys page for the key vault 54 | | storage_account| **[Your Storage Account Name]** | You can also retrieve the Storage Account name from the Storage Account resource page | 55 | | application_id | **[Your Application ID]** | You can also retrieve this ID from the Microsoft Entra > App registration Overview page | 56 | | directory_id | **[Your Directory (Tenant) ID]** | You can also retrieve this ID from the Microsoft Entra > App registration Overview page | 57 | 58 | {: .note } 59 | > If a value in the **Values** column is missing, use the information from the **Comment** column to retrieve the values that you need. 60 | 61 | 1. In the second code cell, populate the **<ENTERHERE>** placeholders with the following information: 62 | 63 | |Line| Name | Value | 64 | |:---------|:---------|:---------| 65 | |1| container_name | **"medallion"** | 66 | |8| df | **spark.read.format("csv").options(inferSchema="true", header="true").load(path=f"{file_path.path}*")** | 67 | |11| df.createOrReplaceTempView | **df.createOrReplaceTempView(file_path.name.removesuffix('.csv').replace('.', ''))** | 68 | 69 | 1. In the third code cell, replace the **<ENTERHERE>** placeholder with the following command. This command displays available views. 70 | 71 | ``` 72 | SHOW VIEWS 73 | ``` 74 | 75 | 1. In the fourth code cell, replace the **<ENTERHERE>** placeholder with the following command. This command displays the first 100 rows from salesltaddress. 76 | 77 | ``` 78 | SELECT * FROM salesltaddress LIMIT 100 79 | ``` 80 | 81 | 1. In the fifth code cell, replace the **<ENTERHERE>** placeholder with the following command. 82 | 83 | ``` 84 | spark.sql("SHOW VIEWS") 85 | ``` 86 | 87 | 1. Select **Run all**. 88 | 89 | ![Runall.png](../media/instructions268353/Runall.png) 90 | 91 | 1. Review the output for each cell. -------------------------------------------------------------------------------- /docs/Ex_04/0402.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Verify that delta files were written to the silver medallion container ' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 05: Implement the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers)' 6 | --- 7 | 8 | # Task 02: Verify that delta files were written to the silver medallion container 9 | 10 | 1. Switch to the browser tab for the Azure portal. In the Search field, search for and select **storage@lab.LabInstance.Id**. 11 | 12 | 1. In the left navigation pane, select **Data Storage** and then select **Containers**. 13 | 14 | 1. Select the **medallion** container. Verify that the delta files are present in the **silver** directory. 15 | -------------------------------------------------------------------------------- /docs/Ex_04/0403.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '3. Read from the Silver layer, create a data model (Facts, Dimensions) on the data lake from Azure Databricks, and write data into Gold layer as external tables. (Z-ordered within ADLS Gen2 as the Gold layer)' 3 | layout: default 4 | nav_order: 3 5 | parent: 'Exercise 05: Implement the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers)' 6 | --- 7 | 8 | # Task 03: Read from the Silver layer, create a data model (Facts, Dimensions) on the data lake from Azure Databricks, and write data into Gold layer as external tables. (Z-ordered within ADLS Gen2 as the Gold layer) 9 | 10 | The last step is to develop and implement a robust data model from the Silver layer, moving critical data into the Gold layer, thus bolstering Contoso's analytics and decision-making capabilities with structured and refined data sets. 11 | 12 | 1. Switch back to the **Databricks** browser tab. 13 | 14 | 1. On the **Databricks** page, in the left navigation pane, select **Workspace**. 15 | 16 | 1. In the Users workspace, select the **Silver to Gold** notebook. 17 | 18 | 1. In the first code cell, populate the **ENTERHERE** placeholders with the following information. 19 | 20 | | Name | Value |Comment| 21 | |:---------|:---------|:---------| 22 | | scope | **scopesecret** || 23 | | key | **appServiceRegistrationSecret** |You can also retrieve the secret from the Keys page for the key vault | 24 | | storage_account | **[Your Storage Account Name]** |You can also retrieve the Storage Account name from the Storage Account resource page | 25 | | application_id | **[Your Application ID]** | You can also retrieve this ID from the Microsoft Entra > App registration Overview page | 26 | | directory_id | **[Your Directory (Tenant) ID]** | You can also retrieve this ID from the Microsoft Entra > App registration Overview page | 27 | 28 | 29 | 1. In the second code cell, replace the **ENTERHERE** placeholder with **medallion**. 30 | 31 | 32 | 1. In the sixth code cell, replace the **<ENTERHERE>** placeholder with the following code: 33 | 34 | ``` 35 | "select dp.ProductIDKey, ds.CustomerIDKey, soh.*, sod.OrderQty, sod.ProductID, sod.UnitPrice, sod.UnitPriceDiscount, sod.LineTotal from salesorderheader soh join salesorderdetail sod on soh.SalesOrderID = sod.SalesOrderID LEFT JOIN dimProduct dp ON sod.ProductID = dp.ProductID LEFT JOIN dimCustomer ds ON soh.CustomerID = ds.CustomerID" 36 | ``` 37 | 38 | 1. In the thirteenth code cell, populate the **ENTERHERE** placeholder to create the df_factSales dataframe: 39 | 40 | ``` 41 | select dp.ProductIDKey, ds.CustomerIDKey, soh.*, sod.OrderQty, sod.ProductID, sod.UnitPrice, sod.UnitPriceDiscount, sod.LineTotal from salesorderheader soh join salesorderdetail sod on soh.SalesOrderID = sod.SalesOrderID LEFT JOIN dimProduct dp ON sod.ProductID = dp.ProductID LEFT JOIN dimCustomer ds ON soh.CustomerID = ds.CustomerID 42 | ``` 43 | 44 | 1. Select **Run all**. 45 | 46 | 1. Review the output for each cell. 47 | -------------------------------------------------------------------------------- /docs/Ex_04/0404.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '4. Verify that delta files were written to the gold medallion container ' 3 | layout: default 4 | nav_order: 4 5 | parent: 'Exercise 05: Implement the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers)' 6 | --- 7 | 8 | # Task 04: Verify that delta files were written to the gold medallion container 9 | 10 | 1. Switch to the browser tab for the Azure portal. In the search box, search for and select your **[Storage Account Name]**. 11 | 12 | 1. In the left navigation pane, select **Data Storage** and then select **Containers**. 13 | 14 | 1. Select the **medallion** container. Verify that the delta files are present in the **gold** directory. 15 | 16 | 1. Open a new browser tab and go to **https://app.powerbi.com/** 17 | 18 | 1. In the lower left of the navigation pane, select **Power BI** and then select **Data Factory**. 19 | 20 | 1. In the left navigation, close the three Lakehouses by selecting the **X**. 21 | 22 | ![removelakehouses.jpg](../media/instructions257645/removelakehouses.jpg) 23 | 24 | 1. Leave the page open. 25 | -------------------------------------------------------------------------------- /docs/Ex_04/Ex04_implement_medallion_architecture.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 05: Implement the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers)' 3 | layout: default 4 | nav_order: 6 5 | has_children: true 6 | --- 7 | 8 | # Exercise 05: Implement the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers) 9 | 10 | This exercise revolves around implementing the Medallion Architecture utilizing Azure Databricks, with a particular emphasis on its Bronze, Silver, and Gold layers. 11 | 12 | The Medallion Architecture is a data processing paradigm where raw data is ingested into the Bronze layer, transformed and curated in the Silver layer, and then aggregated and analyzed in the Gold layer. 13 | 14 | In this context, Azure Databricks serves as the platform for executing data transformations, leveraging its powerful analytics capabilities to process data from the Bronze layer, convert it into a delta format, and subsequently store it in the Silver layer for further refinement and analysis. 15 | -------------------------------------------------------------------------------- /docs/Ex_05/0501.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Create a shortcut from the Lakehouse pointing to the Gold layer' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 06: Serving data with Microsoft Fabric' 6 | --- 7 | 8 | # Task 01: Create a shortcut from the Lakehouse pointing to the Gold layer 9 | 10 | This task involves setting up a Lakehouse architecture within the Microsoft Fabric ecosystem. Then, with the Lakehouse established, the next step is to ensure that it is seamlessly integrated with the existing data layers, particularly the Gold layer in Azure Data Lake Storage (ADLS) Gen 2. 11 | 12 | The Gold layer houses Contoso's most refined and valuable data, ready for business intelligence and analytics. Creating a shortcut to this layer within the Lakehouse framework ensures that users and applications can access the most pertinent data without navigating through the complexities of the data storage hierarchy. 13 | 14 | 1. Open a new browser tab and go to **[https://app.powerbi.com/](https://app.powerbi.com/)**. 15 | 16 | 1. In the left menu pane, close the three Lakehouses by selecting the **X** next to each Lakehouse. 17 | 18 | ![removelakehouses.jpg](../media/instructions257645/removelakehouses.jpg) 19 | 20 | 1. In the left menu pane, select the **ContosoSales@lab.LabInstance.Id** workspace. 21 | 22 | 1. In the upper left of the **ContosoSales@lab.LabInstance.Id** workspace, select **+ New item**. In the Get data section, select **Lakehouse**. 23 | 24 | 1. Create a new Lakehouse named **gold_shortcut**. 25 | 26 | 1. On the **Explorer** blade, under **gold_shortcut**, select the ellipses (**....**) next to **Tables**. 27 | 28 | ![aa22.jpg](../media/instructions270841/aa22.jpg) 29 | 30 | 1. Select **New shortcut**. 31 | 32 | 1. In the External sources section, select the **Azure Data Lake Storage Gen2** tile. 33 | 34 | 1. In the **New shortcut** window, select **Existing connection** and select the connection. 35 | 36 | {: .note } 37 | > If the existing connection is not listed or if you’re no longer signed in, enter the URL for your storage account in the URL box (e.g., https://your-storage-account-name.dfs.core.windows.net/) and then select Sign in. Then, select your account name. 38 | 39 | 1. On the **Connection** menu, select the previously created connection and select **Next**. 40 | 41 | 1. In the left menu, expand **medallion** > **gold**, select the following checkboxes, and then select **Next**: 42 | 43 | - **dimCustomer** 44 | - **dimProduct** 45 | - **dimDate** 46 | - **factSales** 47 | 48 | 49 | ![3lq6s0xq.jpg](../media/instructions257645/3lq6s0xq.jpg) 50 | 51 | 52 | 1. Select **Create**. 53 | 54 | ![lcp8qyu0.jpg](../media/instructions257645/lcp8qyu0.jpg) 55 | 56 | {: .warning } 57 | > If the list of shortcuts does not display on the page, wait for a minute and then refresh the page. 58 | -------------------------------------------------------------------------------- /docs/Ex_05/0502.md: -------------------------------------------------------------------------------- 1 | 30 | -------------------------------------------------------------------------------- /docs/Ex_05/Ex05_serving_data.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 06: Serving data with Microsoft Fabric' 3 | layout: default 4 | nav_order: 7 5 | has_children: true 6 | --- 7 | 8 | # Exercise 06: Serving data with Microsoft Fabric 9 | 10 | This exercise focuses on operationalizing the data within Contoso's ecosystem. Contoso leverages Microsoft Fabric's data management and orchestration capabilities to streamline the integration, processing, and serving of data across their enterprise. 11 | -------------------------------------------------------------------------------- /docs/Ex_06/0601.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Create a new semantic model and choose required tables' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 07: Create PBI reports' 6 | --- 7 | 8 | # Task 01: Create a new semantic model and choose required tables 9 | 10 | Begin by crafting a new semantic model in Power BI, selecting the necessary tables from the data Lakehouse. This model serves as the foundation for all further analysis, encapsulating the logic and structure needed to explore Contoso's data effectively. 11 | 12 | 1. In the upper-right, next to **Share**, select the menu and select **SQL analytics endpoint**. 13 | 14 | ![5bxsy6y0.jpg](../media/instructions257645/5bxsy6y0.jpg) 15 | 16 | 1. In the lower-left, select **Model** to change the view. 17 | 18 | ![v4pfpe1k.jpg](../media/instructions257645/v4pfpe1k.jpg) 19 | 20 | 1. In the upper-left, select **Reporting** and then select **Manage default semantic model**. 21 | 22 | 1. In the **Manage default semantic model** dialog box, select **Select all** and then select **Confirm**. 23 | 24 | ![oqq6n4br.jpg](../media/instructions257645/oqq6n4br.jpg) 25 | 26 | >{: .warning } 27 | > Wait for the semantic model to complete. 28 | -------------------------------------------------------------------------------- /docs/Ex_06/0602.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Create relationships' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 07: Create PBI reports' 6 | --- 7 | 8 | # Task 02: Create relationships 9 | 10 | Establish relationships between the tables within the semantic model. This step is crucial for creating a coherent data model that accurately reflects the intricacies of Contoso's operations, ensuring that the reports generated provide meaningful insights. 11 | 12 | 1. In the upper left, select **Home**. 13 | 14 | 1. Under the **Home** tab, on the menu, select the **Refresh** icon. 15 | 16 | ![refresh3.jpg](../media/instructions254096/refresh3.jpg) 17 | 18 | >{: .warning } 19 | > This refresh is required for the workspace to show the proper objects. 20 | 21 | >{: .note } 22 | > There should be four tables in the view. Use the Scale sliders to adjust the view as necessary. 23 | 24 | 1. Drag **factSales** > **CustomerIDKey** to **dimCustomer** > **CustomerIDKey** to create the relationship between the two tables. 25 | 26 | ![dragtableitemx.gif](../media/instructions254096/dragtableitemx.gif) 27 | 28 | 1. In the **New relationship** window, select the **Assume referential integrity** checkbox and then select **Save**. 29 | 30 | 1. Drag **factSales** > **ProductIDKey** to **dimProduct** > **ProductIDKey** 31 | 32 | 1. In the **New relationship** window, select **Assume referential integrity** and then select **Save**. 33 | 34 | 1. Drag **factSales** > **OrderDate** to **dimDate** > **Date**. 35 | 36 | >{: .warning } 37 | > On the following **New relationship window**, **DO NOT** select the **Assume referential integrity.** option. 38 | 39 | > ![new-relationship.png](instructions257645/new-relationship.png) 40 | 41 | 1. On the **New relationship** window, keep the default settings and select **Save**. 42 | -------------------------------------------------------------------------------- /docs/Ex_06/0603.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '3. Create a PowerBI report point to the semantic model (using direct lake mode)' 3 | layout: default 4 | nav_order: 3 5 | parent: 'Exercise 07: Create PBI reports' 6 | --- 7 | 8 | # Task 03: Create a PowerBI report point to the semantic model (using direct lake mode) 9 | 10 | With the model and relationships set up, create a Power BI report that points directly to the semantic model, utilizing Direct Query mode to access the data lake. This approach allows Contoso to generate real-time insights from their data, supporting dynamic decision-making processes and fostering a culture of informed strategy development across the organization. 11 | 12 | 1. On the menu above the canvas, select **New report**. 13 | 14 | 1. Once the **Upgrade to Power BI Pro License** message appears, select **Try free**. 15 | 16 | ![upgradetoPAIDpowerBI.jpg](../media/instructions254096/upgradetoPAIDpowerBI.jpg) 17 | 18 | 1. In the **All paid features of Power BI are yours for 60 days** dialog box, select **Got it**. 19 | 20 | ![paidfeatures60days.jpg](../media/instructions254096/paidfeatures60days.jpg) 21 | 22 | 1. On the **Visualizations** pane, select the **Stacked column chart**. 23 | 24 | ![stacked-column-chart.png](../media/instructions257645/stacked-column-chart.png). 25 | 26 | >{: .note } 27 | >You can drag the chart to expand its size or select **Focus mode** to make the visualization fill the pane. 28 | 29 | ![Focus-mode.png](../media/instructions257645/Focus-mode.png) 30 | 31 | 1. On the **Data** pane, select the **dimCustomer** > **CompanyName** checkbox. 32 | 33 | 1. On the **Data** pane, select the **factSales** > **TotalDue** checkbox. 34 | 35 | 1. On the **Filters** pane, select the **CompanyName is (All)** filter card. 36 | 37 | 1. Select the **Filter type** menu and then select **Top N**. 38 | 39 | 1. In the **Show items** box, enter **10**. 40 | 41 | 1. On the **Data** pane, select **factSales** > **TotalDue** and drag it to the **By value** box on the filter card. 42 | 43 | ![rport.jpg](../media/instructions254096/rport.jpg) 44 | 45 | 1. On the **Visualizations** pane, under **Build visual**, select **Format your Visual**. 46 | 47 | 1. Select the **General** tab and then expand **Title**. 48 | 49 | 1. In the **Text** box enter **Top 10 Customers**. 50 | 51 | >{: .note } 52 | > These are the top 10 companies. 53 | 54 | 1. Try adding to this report on your own. 55 | 56 | >{: .note } 57 | > Add a report for the top 10 companies based on sales revenue and the top five products sold by each company. 58 | -------------------------------------------------------------------------------- /docs/Ex_06/Ex06_create_pbi_reports.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 07: Create PBI reports' 3 | layout: default 4 | nav_order: 8 5 | has_children: true 6 | --- 7 | 8 | # Exercise 07: Create PBI reports 9 | 10 | This exercise bridges the gap between raw data and actionable insights, enabling Contoso to create powerful, data-driven reports. Through this exercise, we see how Contoso is moving from data consolidation and integration to the active deployment of insights across the business, all enabled by the advanced analytics and visualization capabilities of Power BI. 11 | -------------------------------------------------------------------------------- /docs/Ex_07/0701.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '1. Create a new semantic model and choose required tables' 3 | layout: default 4 | nav_order: 1 5 | parent: 'Exercise 08: Use PowerBI Copilot' 6 | --- 7 | 8 | # Task 01: Interact with Copilot 9 | 10 | For starters, challenge Copilot to create the same top 10 companies based on sales revenue report and the top five products sold by each company report discussed in the previous Exercise. 11 | 12 | 1. On the menu above the **Filters**, **Visualizations**, and **Data** panes, select **Copilot**. 13 | 14 | ![copilot.jpg](../media/instructions254096/copilot.jpg) 15 | 16 | 1. On the **Copilot** pane, select **Get started**. 17 | 18 | ![copilotgetstarted.jpg](../media/instructions254096/copilotgetstarted.jpg) 19 | 20 | 1. Select **Create a new report page** and then, in the prompt, enter the following: 21 | 22 | ```Copilot-wrap 23 | that shows the top 10 companies based on sales revenue and the top 5 products sold by each company. 24 | ``` 25 | 1. Select **Send**. 26 | 27 | 1. When prompted for the title and description of the page, enter the following and then select **Send**: 28 | 29 | ```Copilot 30 | Top Revenue and Products 31 | ``` 32 | 33 | 1. Review the page that Copilot produced. See below for an example page. 34 | 35 | ![examplechartpage.jpg](../media/instructions254096/examplechartpage.jpg) 36 | 37 | 1. On your own, modify one of the visuals in the page and review the changes. 38 | -------------------------------------------------------------------------------- /docs/Ex_07/0702.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: '2. Create a page about Profit Margin' 3 | layout: default 4 | nav_order: 2 5 | parent: 'Exercise 08: Use PowerBI Copilot' 6 | --- 7 | 8 | # Task 02: Create a page about Profit Margin 9 | 10 | 1. Close the Copilot pane and re-open it to start over. 11 | 12 | 1. In the prompt, enter the following and then select **Send**: 13 | 14 | 15 | ```Copilot-wrap 16 | Create a new page named Overview of Profit Margin to analyze the total sales, average price, and profit margin of different products and product categories over time and across regions. 17 | ``` 18 | 19 | 1. Review the results. 20 | 21 | ![2ndresult.jpg](../media/instructions254096/2ndresult.jpg) 22 | 23 | 1. Use Copilot to suggest content for this report. On the **Copilot** pane, under **Here are some things you can try**, select **Suggest content for this report**. 24 | 25 | 1. On your own, follow the prompts and choose one of the suggestions provided by Copilot, then select the pencil to enter the text into the prompt. 26 | 27 | ![lastreport.jpg](../media/instructions254096/lastreport.jpg) 28 | 29 | **Congratulations! You have completed the Microsoft Fabric and Azure Databricks lab!** 30 | -------------------------------------------------------------------------------- /docs/Ex_07/Ex07_use_powerbi_copilot.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'Exercise 08: Use PowerBI Copilot' 3 | layout: default 4 | nav_order: 9 5 | has_children: true 6 | --- 7 | 8 | # Exercise 08: Use PowerBI Copilot 9 | 10 | Introducing Microsoft Fabric Copilot, your ultimate co-pilot in code creation. With advanced AI assistance, Fabric Copilot revolutionizes the way developers work, providing insightful suggestions and code snippets to streamline your workflow. Say goodbye to tedious debugging and hello to efficient coding with Microsoft Fabric Copilot by your side. Experience the future of programming today. 11 | 12 | Unlock the power of real-time analytics with PowerBI Copilot. Seamlessly integrate AI assistance into your data visualization process, empowering you to uncover actionable insights faster than ever before. From data exploration to dynamic dashboard creation, PowerBI Copilot accelerates your analytics journey, driving informed decision-making at every turn. 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/index.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Introduction 3 | layout: home 4 | nav_order: 1 5 | --- 6 | 7 | # TechExcel: Microsoft Fabric with Azure Databricks for Data Analytics (lvl 300 / CSU) lab 8 | 9 | 10 | **The estimated time to complete this lab is six hours.** 11 | 12 | ## Overview 13 | 14 | ![ArchitectureDiagramMFADBNew.png](docs/media/instructions240153/ArchitectureDiagramMFADBNew.png) 15 | 16 | This lab showcases Microsoft Fabric and Azure Databricks, featuring a performance-optimized and cloud-native analytics solution pattern. This architecture unifies a customer’s data estate to accelerate data value creation. 17 | 18 | The visual illustrates the real-world example for Contoso, a fictitious company. Contoso is a retailer with thousands of brick-and-mortar stores across the world. They also have an online store. Contoso is acquiring Litware Inc. Litware has curated marketing data and sales data processed by Azure Databricks and stored in the gold layer in ADLS Gen 2. 19 | During our exercises, we’ll see how they leveraged the power of Microsoft Fabric to ingest data from disparate sources, combine data with their existing data from ADLS Gen2, and derive meaningful insights. You’ll witness how the team used a shortcut to reference the existing Litware data from ADLS Gen2. You’ll also see how they mounted the OneLake endpoint in Azure Databricks to derive meaningful insights using compute in Azure Databricks. 20 | 21 | The lab scenario starts on January 30th. The company's new CEO, April, recently noticed negative trends in their KPIs, including: 22 | 23 | * High customer churn 24 | * Declining sales revenue 25 | * High bounce rate on their website 26 | * High operating expenses 27 | * Poor customer experience 28 | 29 | April asks Rupesh, the Chief Data Officer how they could create a data-driven organization and reverse these adverse KPI trends. Rupesh talks to his technical team, including Eva, the data engineer, Miguel, the data scientist, and Wendy, the business analyst, to design and implement a solution pattern to realize this dream of a data-driven organization. Our story is centered around Rupesh and his team. They recognize that the existence of data silos within Contoso's various departments presents a significant integration challenge. 30 | 31 | During this lab, as a part of this team, you’ll execute some of these steps to reverse these adverse KPI trends. 32 | 33 | Here are the Microsoft Fabric workloads showcased in this solution along with Azure Databricks. 34 | 35 | - Synapse Data Engineering 36 | - Data Factory 37 | - Synapse Data Science 38 | - Synapse Data Warehouse 39 | - Power BI 40 | - Synapse Real-time Analytics 41 | 42 | 43 | ## Exercises 44 | 45 | This lab has exercises on: 46 | 47 | - Provision and configure resources 48 | - Create a Fabric workspace and data pipeline 49 | - Expore Copilot capabilites 50 | - Establishing connectivity between Azure Databricks and Azure Data Lake Storage (ADLS) Gen 2 51 | - Implementing the Medallion Architecture using Azure Databricks (Bronze, Silver and Gold layers) 52 | - Serving data with Microsoft Fabric 53 | - Creating PBI reports 54 | - Use PowerBI Copilot 55 | 56 | 57 | ## Disclaimer 58 | 59 | This presentation, demonstration, and demonstration model are for informational purposes only and (1) are not subject to SOC 1 and SOC 2 compliance audits, and (2) are not designed, intended, or made available as a medical device(s) or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Microsoft makes no warranties, express or implied, in this presentation, demonstration, and demonstration model. Nothing in this presentation, demonstration, or demonstration model modifies any of the terms and conditions of Microsoft’s written and signed agreements. This is not an offer, and applicable terms and the information provided are subject to revision and may be changed at any time by Microsoft. 60 | 61 | This presentation, demonstration, and demonstration model do not give you or your organization any license to any patents, trademarks, copyrights, or other intellectual property covering the subject matter in this presentation, demonstration, and demonstration model. 62 | 63 | The information contained in this presentation, demonstration, and demonstration model represents the current view of Microsoft on the issues discussed as of the date of presentation and/or demonstration, for the duration of your access to the demonstration model. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information presented after the date of presentation and/or demonstration and for the duration of your access to the demonstration model. 64 | 65 | No Microsoft technology, nor any of its component technologies, including the demonstration model, is intended or made available as a substitute for the professional advice, opinion, or judgment of (1) a certified financial services professional, or (2) a certified medical professional. Partners or customers are responsible for ensuring the regulatory compliance of any solution they build using Microsoft technologies. 66 | 67 | ## Copyright 68 | 69 | © 2023 Microsoft Corporation. All rights reserved.  70 | 71 | By using this demo/lab, you agree to the following terms: 72 | 73 | The technology/functionality described in this demo/lab is provided by Microsoft Corporation for purposes of obtaining your feedback and to provide you with a learning experience. You may only use the demo/lab to evaluate such technology features and functionality and provide feedback to Microsoft. You may not use it for any other purpose. You may not modify, copy, distribute, transmit, display, perform, reproduce, publish, license, create derivative works from, transfer, or sell this demo/lab or any portion thereof. 74 | 75 | COPYING OR REPRODUCTION OF THE DEMO/LAB (OR ANY PORTION OF IT) TO ANY OTHER SERVER OR LOCATION FOR FURTHER REPRODUCTION OR REDISTRIBUTION IS EXPRESSLY PROHIBITED. 76 | 77 | THIS DEMO/LAB PROVIDES CERTAIN SOFTWARE TECHNOLOGY/PRODUCT FEATURES AND FUNCTIONALITY, INCLUDING POTENTIAL NEW FEATURES AND CONCEPTS, IN A SIMULATED ENVIRONMENT WITHOUT COMPLEX SET-UP OR INSTALLATION FOR THE PURPOSE DESCRIBED ABOVE. THE TECHNOLOGY/CONCEPTS REPRESENTED IN THIS DEMO/LAB MAY NOT REPRESENT FULL FEATURE FUNCTIONALITY AND MAY NOT WORK THE WAY A FINAL VERSION MAY WORK. WE ALSO MAY NOT RELEASE A FINAL VERSION OF SUCH FEATURES OR CONCEPTS. YOUR EXPERIENCE WITH USING SUCH FEATURES AND FUNCITONALITY IN A PHYSICAL ENVIRONMENT MAY ALSO BE DIFFERENT. 78 | 79 | --------------------------------------------------------------------------------