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To get started with Microsoft Fabric, build a small analytics project in this order: create a workspace, make a lakehouse, load and shape sample data, create a semantic model, and build a report. Copilot is a separate step: for the Power BI experiences covered by Microsoft’s current guidance, a Pro or Premium Per User license alone does not qualify. Your organization also needs eligible paid capacity, administrator enablement, a supported region, and a workspace assigned to that capacity.
What you need before starting
Fabric brings data engineering, storage, analytics, and Power BI reporting into one environment. For a first project, use a nonproduction workspace and sample data so you can learn the flow without relying on it for business decisions.
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- For the learning project: access to a Fabric workspace and sample data. Microsoft’s tutorials walk through a lakehouse-based workflow; the tutorial’s availability may depend on your organization’s Fabric access.
- For Copilot: organizational configuration in addition to user access. Microsoft’s documented Power BI Copilot requirements include paid Fabric F2-or-higher or Power BI Premium P1-or-higher capacity, an administrator-enabled setting, and a supported region. A Pro or Premium Per User license by itself is not a substitute for that capacity. Check the requirements for the particular Copilot experience because availability and eligibility can vary. Microsoft’s Fabric Copilot setup guidance and the Power BI Copilot overview describe the documented prerequisites.
Build a first Fabric analytics project
A practical starter path is workspace → lakehouse → data ingestion and transformation → semantic model → report. Microsoft’s end-to-end Power BI report tutorial uses Dataflows Gen2 to prepare and load data, and a pipeline can orchestrate repeatable refresh. You can begin without automation, then add it when you need the flow to run again.
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- Open or create a workspace. Use a learning or nonproduction workspace that your organization permits you to use. If you do not have access, ask your Fabric administrator to provide it.
- Create a lakehouse. Use the workspace’s new-item options to create a lakehouse. A lakehouse gives the project a place to store its data and supports the tutorial’s data-preparation path.
- Load sample data and inspect it. Follow the tutorial to bring data into the lakehouse. Check column names, data types, missing values, and whether rows represent the level of detail you expect before building reports on top of them.
- Transform and, if needed, automate ingestion. Use Dataflows Gen2 to prepare data. Add a pipeline if you want to orchestrate the work and make refresh repeatable. Keep the first version small: automate only after you understand the steps and expected output.
- Create a semantic model. Select the tables that reports need and define relationships that reflect the facts and dimensions in your data. Microsoft documents Direct Lake for lakehouse models; Import and DirectQuery are also available in relevant Fabric data-source cases. The right mode depends on the source, freshness needs, and model design, not on a universal rule. See Microsoft’s semantic-model guidance.
- Build a report and validate it. Create a report from the semantic model. Verify important totals and relationships against the source data or known business definitions before using the report to explain results or asking Copilot to do so.
Choose a workflow that fits the project
A lakehouse tutorial is a useful first route, but not every analytics project needs the same architecture. Decide based on how data arrives, how often it must refresh, what kinds of data work you need to do, and how reporting should consume it.
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| Question | What to consider |
|---|---|
| Does data need to be loaded repeatedly? | For a one-time learning exercise, manual or tutorial-led ingestion may be enough. For repeatable refresh, use Dataflows Gen2 and consider a pipeline for orchestration. |
| How much transformation flexibility is needed? | Consider whether the work is best handled through Dataflows Gen2, SQL, or Spark in your environment. The cited tutorials establish a lakehouse path, not one best tool for every transformation. |
| How should reports access data? | Direct Lake is documented for lakehouse models; Import and DirectQuery apply in relevant cases. Evaluate freshness, source, and model needs before choosing. |
Microsoft’s lakehouse tutorial and semantic-model documentation provide starting points for these decisions.
Check Copilot eligibility and configure access
Copilot availability is an organization-level deployment question as well as a user-level access question. Before planning around it, confirm the requirements for the specific workload and feature in Microsoft’s live documentation. The Power BI guidance cited here specifies paid Fabric F2-or-higher or Power BI Premium P1-or-higher capacity, supported-region availability, and administrator enablement; sovereign clouds are not supported for the documented requirements. SKU, region, and feature eligibility can change.
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- Confirm the experience and capacity. Ask the Fabric or Power BI administrator which Copilot capability you intend to use and whether the organization’s capacity meets that experience’s current requirements. Do not assume Pro or PPU alone is sufficient.
- Review tenant settings. In the Fabric admin portal, the administrator reviews and enables the relevant Copilot setting. Microsoft allows settings to be scoped to security groups; some settings can also be delegated to capacity administrators. See Enable and configure Copilot in Microsoft Fabric and Enable Fabric Copilot for Power BI.
- Review data-processing geography. Depending on location and tenant settings, enabling Copilot may require allowing data sent to Azure OpenAI to be processed outside the capacity’s geographic region or compliance boundary. The tenant administrator should review this with privacy and compliance owners before enabling the setting.
- Enable capacity-level access if required. If tenant settings are delegated, the capacity administrator may also need to enable Copilot at the capacity level.
- Assign the workspace and grant user access. Associate Copilot-enabled items with the supported capacity and ensure users have the required workspace permissions. In Power BI Desktop, select a compatible Copilot-enabled workspace.
- Roll out deliberately. Start with an intended user group and monitor capacity use. Microsoft cautions that enabling Copilot broadly without planning can increase capacity utilization.
Prepare the semantic model before asking Copilot
Copilot works against the definitions and structure available to it; it does not replace sound data modeling or result validation. Microsoft warns that unprepared data, models, or users can lead to inaccurate or misleading responses. Its semantic-model preparation guidance recommends improving the model and checking answers rather than treating generated output as authoritative.
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- Document verified answers. Add answers for important, recurring business questions where the feature supports them, using definitions validated by the people responsible for the data.
- Write AI instructions. Explain relevant business terms, metrics, and interpretation rules so Copilot has appropriate context.
- Test representative questions. Compare responses with known results and business definitions. If an answer differs, investigate the data, relationships, definitions, or instructions and revise the model as needed.
- Use approval controls where available. Microsoft’s model-preparation tutorial covers marking a model approved for Copilot; some preparation capabilities may be in preview, so confirm their current status before depending on them.
For the setup and preparation walkthrough, see Use Copilot with semantic models in Power BI.
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What Copilot can do in Data Factory
Copilot is not one identical feature across Fabric. Microsoft’s Data Factory Copilot guide describes workload-specific assistance such as natural-language support for data integration solutions, transformation-query assistance in Dataflow Gen2, pipeline generation and summaries, and troubleshooting recommendations. Feature availability and preview status vary. Check the current documentation for the workload you plan to use, and do not base a production design on a capability until its status and requirements are confirmed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the learning project separate from Copilot deployment
You can learn the Fabric project flow by creating a small lakehouse, shaping sample data, building a semantic model, and checking a report. Copilot adds separate capacity, tenant, region, workspace, and governance requirements. Treat those as an administrator-led rollout, then prepare and validate the model before inviting users to rely on generated analytics.
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- Brilliant Display – Stunning 13.8" PixelSense touchscreen[1], with brilliant LCD display[2], unleashes luminous whites, deeper blacks and colors so richly saturated bringing vivid life into every frame – perfect for work, school, streaming and creative tasks.
- Power that lasts all day – With 20 hours of battery life[3], the new Surface Laptop powers through your entire day, so you can create, work and stream from morning to night without reaching for a charger.
- Work at the speed of your ideas – Built with the latest Qualcomm Snapdragon X2 Elite (12 Core) processors, Surface Laptop delivers fast, AI‑accelerated performance—making it the most powerful Surface laptop for everything from multitasking to demanding workloads.
- The ports you need – Charge on-the-go, transfer data fast, or create the ultimate desktop set up with two USB-C / USB4[4] ports.
- Built-in AI Companion – Work smarter, create freely, and communicate with confidence—Copilot[5] on Windows 11 is always there to help.
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- 19 HOURS OF BATTERY (LEAVE THE CHARGER) — Up to 19 hours of video playback[3] on a single charge. Work from a coffee shop, take it to class/work, or binge an entire season on a long flight — it'll keep up.
- Two USB-C / USB4[4] ports and a microSD card reader for fast charging, big file transfers, or hooking up to three 4K monitors when you want a full desktop. Wi-Fi 7 keeps you online and fast wherever you are.
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