Microsoft Fabric  

Mastering PySpark's EXPLODE Function in Fabric Notebooks

Nested data structures can be a challenge, especially when working with arrays or maps inside Microsoft Fabric Notebooks. PySpark’s explode function is a powerful tool that allows data professionals to transform complex, hierarchical datasets into structured, analysis-ready formats—unlocking new possibilities for Fabric Warehouse, OneLake, and Delta Lake integration. In this article, I will walk you through how to use the EXPLODE function to unwrap arrays into rows.

Why explode is a Game-Changer in Fabric Notebooks?

Structured and semi-structured data often contain nested fields—arrays or maps—that can hinder traditional querying and aggregations. Whether you're dealing with streaming IoT telemetry, customer behavior logs, or hierarchical JSON responses from APIs, the ability to flatten these structures into atomic elements is a paradigm shift for optimized data processing.

Microsoft Fabric, with its unified analytics ecosystem, provides a robust environment to leverage PySpark’s capabilities for deep data transformations. Using explode, data professionals can drive:

  • Columnar granularity for enhanced query performance

  • Advanced analytics through distributed computation

  • Seamless integration with Fabric Warehouse for structured data management

Implementation

For the demonstration, I've got a delta table in my Lakehouse (folks) which contains two columns such as Name and Hobbies. The Hobbies contains array values separated by commas for each names in the first column. The table is read to the Fabric Notebook using spark.read.format method. The goal is to expand each of the hobbies into separate rows

To expand each of the hobbies into seprate rows, I execute this code

from pyspark.sql.functions import col, explode, split

df = df.withColumn("Hobbies_Array", split(col("Hobbies"), ","))


df_exploded = df.withColumn("New_Hobbies", explode(col("Hobbies_Array")))

display(df_exploded.select(col("Name"),col("New_Hobbies")))

🛠 Breakdown:

  1. Column Converstion - the hobbies column was converted from STRING to ARRAY

  2. Explode Function Application – Each array hobbies is expanded into separate rows.

  3. Optimized Querying – The df_exploded DataFrame displayed the selected Name and New_Hobbies columns

🌐 Real-World Applications

  • Streaming Analytics: Expanding nested event logs for real-time monitoring.

  • IoT & Edge Computing: Breaking down sensor data for actionable insights.

  • Customer 360°: Flattening multi-channel user interactions for holistic modeling.

See you in the next article

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