Microsoft Fabric Overview

Microsoft Fabric Overview

PRASAD DHANVE PRASAD DHANVE

PRASAD DHANVE

NSCC | Business Intelligence and Analytics | Proficient in SQL, Python, Power BI, SSIS, Power Query, Excel, and Tableau

发布日期: 2024年4月12日
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This project showcases the capabilities of Microsoft Fabric, demonstrating end-to-end solutions within a single environment. It covers workspace creation, lakehouse setup, data wrangling, modeling, SQL querying for insights, and Power BI integration.

Project Steps

  1. Workspace Creation
  2. lakehouse setup
  3. Notebook creation
  4. Data Preparation
  5. Data Wrangler tool
  6. Data Conversion and Modelling
  7. SQL Querying
  8. Semantic Model for Power BI
  9. Data Validation

1. Workspace Creation

  • Created a workspace within the Microsoft Fabric environment named RestaurantRevenue.
  • Under this Workspace I can perform all the operations on my data end to end.

2. Lakehouse Setup

  • Established a lakehouse under the workspace named Technical_Presentation.
  • Where I can store big data for cleaning, querying, reporting, and sharing.
  • Can load data using various options.

3. Notebook Creation

  • Added a new notebook under the lakehouse for data operations using an open notebook.

  • Where we get an option to select the language using a dropdown handle. There we can use different languages under the same notebook specifying the language for the cell.
  • Available languages are Python, Scala, SQL, and R.

  • Under the notebook, we have other options such as the Microsoft data wrangler tool to generate Python code for basic wrangling. Integrating Co-pilot, Creating a Data Pipeline, Opening a VS Code.

4. Data Preparation

  • Installed necessary libraries and imported a dataset from Kaggle using the Kaggle library.
  • Performed data wrangling to convert the dataset into a star schema.

Connecting Notebook directly to Kaggle and loading dataset into the lakehouse as CSV

领英推荐

5. Data Wrangler Tool.

  • Utilized the data wrangler tool available in the Fabric environment for basic data operations.

few options to generate code using data Wrangler. Can explore other capabilities by clicking on the image.
code generated by the data wrangler to drop duplicate categories of Cusine_Type

6. Data Conversion and Modeling

  • Converted the dataset into fact and dimension tables.
  • Assigned different data frames for structured storage.
  • Used PySpark to convert data frames into lakehouse tables.

Loaded Data into the Lakehouse

7. SQL Querying

  • Leveraged SQL endpoints to gather insights from the data by answering specific questions using SQL queries inside the fabric environment.

SQL Endpoint interface inside fabric.

8. Semantic Model for Power BI

  • Created a semantic model to load data into Power BI within the Fabric environment.

Semantic Model

9. Data Validation

  • Plotted a graph on Power BI inside the fabric environment to ensure proper data modeling inside the semantic model.

Scatter plot using power BI inside Fabric.

Thank you for having a look at my project. You can access the complete code used in this project by checking out my GitHub repository.


Joris van Hu?t

Marketing Systems Architect | I Build Predictable Revenue Engines for Scale-Ready Brands | No ROI = No Invoice

11 个月

Used Fabric to streamline our marketing attribution, boosting campaign outcomes. Any specific features you found most impactful?

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Olawole Akomolafe

Business Intelligence Analyst | Financial Analyst | CISRCP | SOX Compliance | CRCMP | Expert in Financial Modeling | Risk Management | SQL | PostgreSQL | Python | Machine Learning | Power BI | ETL | Data-Driven Insights

11 个月

Good job ??

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Marcus Sousa

Data & Analytics Team Lead, BDO Digital

11 个月

Great Job!!

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Adebayo Adeola

11 个月

Nice work, with the excellent training skills of Patrick Dolinger, I do not expect anything less from a BIA graduating student. The ETL, data engineering and analytical features of Fabric make it a must-use in the corporate space.

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