Microsoft Fabric Lakehouse Architecture – The Future of Data Platforms

 🚀 Introduction

Traditionally, organizations used:

• Data lakes → Scalable but unstructured
• Data warehouses → Structured but rigid

Microsoft Fabric introduces Lakehouse Architecture, combining both in one system.

 

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🧠 What is Lakehouse in Fabric?

A Lakehouse:

• Stores all data in one place
• Supports analytics + AI + streaming
• Provides reliability through Delta Lake

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🧱 Key Layers in Fabric Lakehouse

✅ Storage Layer – OneLake

• Unified data lake
• Stores all data types
• Eliminates duplication

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✅ Delta Layer

• Provides ACID transactions
• Ensures data reliability
• Supports time travel

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✅ Compute Layer

• Spark engines for large-scale processing
• SQL engines for analytics

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✅ Consumption Layer

• Power BI dashboards
• AI models
• SQL queries

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🔄 Unified Approach

Unlike traditional systems:

• No data copying
• No separate tools
• No siloed pipelines

👉 Everything runs on a single dataset.

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🎯 Conclusion

The Fabric Lakehouse: 👉 Eliminates silos
👉 Reduces cost
👉 Enables real-time analytics

✅ Making it ideal for modern AI-driven systems

Starter code – build a Lakehouse table and transform it

PySpark – load a CSV and create a Delta table

df = spark.read.format('csv').option('header', 'true').load('Files/sales.csv')
df.write.format('delta').mode('overwrite').save('Tables/sales')

PySpark – filter and write a refined table

from pyspark.sql.functions import col

df = spark.read.format('delta').load('Tables/sales')
df_filtered = df.filter(col('amount') > 100)
df_filtered.write.format('delta').mode('overwrite').saveAsTable('sales_filtered')

SQL – aggregate business metrics

SELECT SUM(amount) AS total_sales
FROM sales_filtered;

Lakehouse Code

 # Create Delta table

df = spark.read.format("csv").option("header","true").load("Files/sales.csv")

df.write.format("delta").save("Tables/sales")

 

# Transform data

from pyspark.sql.functions import col

 df = spark.read.format("delta").load("Tables/sales")

df_filtered = df.filter(col("amount") > 100)

df_filtered.write.saveAsTable("sales_filtered")

 

# Load CSV

 df = spark.read.format("csv").option("header", "true").load("/mnt/raw/sales.csv")

 # Save as Delta

 df.write.format("delta").save("/mnt/delta/sales")

 

# Transform

 from pyspark.sql.functions import col

df_filtered = df.filter(col("amount") > 100)

df_filtered.write.save("/mnt/delta/sales_filtered")

 

# SQL

SELECT SUM(amount) FROM delta.`/mnt/delta/sales`;