Practical Guide: Implementing Data Architectures Using Microsoft Fabric ??

In this guide, we’ll walk through how to implement Data Warehouse, Modern Data Warehouse, and Lakehouse Architectures using Microsoft Fabric. We’ll cover:

? Setting up OneLake as the central data repository

? Implementing ETL pipelines using Data Factory

? Using Delta Lake for scalable Lakehouse processing

? Connecting Power BI for visualization

1. Setting Up Microsoft Fabric and OneLake

Step 1: Enable Microsoft Fabric in Azure

1. Log in to Microsoft Azure Portal.
2. Navigate to Microsoft Fabric Admin Portal → Enable Fabric for your organization.
3. Create a Workspace to manage data projects.

Step 2: Configure OneLake

1. Open Microsoft Fabric → Go to the OneLake section.
2. Create a new lakehouse or warehouse.
3. Upload sample structured (CSV, SQL) and unstructured data (JSON, Parquet).

2. Implementing a Traditional Data Warehouse

Step 1: Create a Warehouse in Fabric

1. In Microsoft Fabric, navigate to Warehouse → Create New SQL Warehouse.
2. Define schema (Tables, Views, Stored Procedures).
3. Use the T-SQL editor to create tables for structured data.

CREATE TABLE SalesData (

???SaleID INT PRIMARY KEY,

???ProductName VARCHAR(100),

???SaleAmount FLOAT,

???SaleDate DATE

);

Step 2: Ingest Data Using Data Factory

1. Go to Data Factory → Create Data Pipeline.
2. Select Azure SQL Database as the source.
3. Choose Fabric Warehouse as the destination.
4. Schedule ETL refresh frequency.

Step 3: Connect Power BI for Analytics

1. Open Power BI → Connect to Fabric Warehouse.
2. Build interactive dashboards using stored data.

? Use Case: A financial institution storing structured transaction data for BI reporting.

3. Implementing a Modern Data Warehouse (Data Lake + Warehouse)

Step 1: Create a Data Lake in OneLake

1. Go to OneLake → Create a Lakehouse.
2. Upload structured, semi-structured, and unstructured files (CSV, JSON, XML).

Step 2: Set Up ETL Pipelines with Data Factory

1. Open Data Factory → Create a new pipeline.
2. Source: Select Azure Blob Storage / OneLake for raw data.
3. Destination: Choose Fabric Warehouse (SQL) or Lakehouse.
4. Transformation: Use Spark Notebooks to clean data before loading.

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DataCleaning").getOrCreate()

df = spark.read.csv("abfss://onelake/data/sales.csv", header=True)

df_cleaned = df.dropna()? # Remove missing values

df_cleaned.write.format("delta").save("abfss://onelake/cleaned_sales")

Step 3: Use Power BI to Analyze Combined Data

1. Open Power BI → Connect to OneLake and Fabric Warehouse.
2. Use composite models to blend structured (SQL) and unstructured (JSON) data.
3. Build AI-powered visualizations using Power BI’s AutoML.

? Use Case: An e-commerce company integrating transactional data (SQL) and customer behavior data (JSON) for product recommendations.

4. Implementing a Lakehouse Architecture (Delta Lake)

Step 1: Set Up a Lakehouse in Microsoft Fabric

1. In Fabric, go to Lakehouse → Click Create New Lakehouse.
2. Define a schema using Delta Tables.
3. Load structured and unstructured data into OneLake (Delta Format).

CREATE TABLE SalesDelta (

????SaleID INT,

????ProductName STRING,

????SaleAmount FLOAT,

????SaleDate DATE

) USING DELTA;

Step 2: Enable ACID Transactions & Real-Time Data Processing

• Use Delta Lake’s ACID capabilities to manage transactional consistency.
• Implement streaming pipelines for real-time data updates.

from delta.tables import DeltaTable

# Load Delta table

deltaTable = DeltaTable.forPath(spark, "abfss://onelake/cleaned_sales")

# Merge new sales data in real-time

deltaTable.alias("old").merge(

????new_sales_data.alias("new"),

????"old.SaleID = new.SaleID"

).whenMatchedUpdateAll().whenNotMatchedInsertAll().execute()

Step 3: Leverage Fabric’s Real-Time Analytics

1. Enable Event Hub in Fabric for streaming data.
2. Connect Kafka or IoT data sources to OneLake.
3. Process real-time event streams using Spark Notebooks.

? Use Case: A ride-sharing company processing real-time trip data and predicting surge pricing using AI.

Choosing the Right Fabric Architecture

?? Microsoft Fabric simplifies data management by integrating ETL, storage, and analytics in a single environment. Here’s a quick decision guide:

• Choose a Fabric Warehouse (SQL) if you need structured BI and reporting.
• Use a Fabric Lakehouse (OneLake + SQL) for mixed workloads (BI + AI).
• Implement a Delta Lake (Lakehouse) for real-time AI-driven analytics.