Python & Spark in Microsoft Fabric

In addition to visual data integration with Data Factory, Microsoft Fabric provides a powerful development environment for data-driven applications: Spark-based notebooks supporting Python, R, SQL, and Scala.

This environment combines the flexibility of open-source technologies with the scalability of the Microsoft cloud. Whether for complex transformations, machine learning pipelines, or exploratory analytics, Apache Spark in Fabric enables efficient processing of large datasets directly in OneLake – without redundant copies or separate clusters.

Fabric therefore becomes an ideal platform for data engineers and data scientists who prefer code-based workflows and require high levels of performance and flexibility. It supports collaborative development across teams with shared workspaces and structured lifecycle management for analytical assets.

Typical Use Cases

  • Data cleansing and enrichment of large raw datasets from multiple sources
  • Transformation of streaming or event data for real-time analytics
  • Development of data science workflows using Python and ML models
  • Processing of unstructured data (e.g., log files, JSON, XML) within OneLake
  • Creation of analytical models that can be directly integrated into Power BI
Graphic representation of connected golden honeycombs – symbolizing distributed data processing with Apache Spark in Microsoft Fabric.

Capabilities

Multicolored puzzle pieces assembled into a structure – symbolizing the integrated Spark runtime environment, Python notebooks, and OneLake data access in Microsoft Fabric.
  • Integrated Spark runtime – no separate cluster management required
  • Support for Python, R, SQL, and Scala in interactive notebooks
  • Direct access to OneLake data (Parquet, Delta Lake, CSV, JSON, and more)
  • Compatibility with common Python libraries (pandas, pyspark, numpy, matplotlib, scikit-learn, mlflow, etc.)
  • Processing of large data volumes in batch or streaming modes
  • Interactive development and debugging within the Fabric interface
  • Integration of results into Power BI and the Data Warehouse
  • Version control and CI/CD via GitHub or Azure DevOps
  • Role-based security and data governance through integration with Microsoft Purview
  • Optimized performance through automatic scaling and distributed processing

Services

Illustration of a workstation with a monitor and the Comelio logo – representing Python and Spark development in Microsoft Fabric for data engineering and machine learning.

We help organizations unlock the full potential of Fabric – from integration and transformation to advanced analytics and machine learning. Our experts combine modern open-source methodologies with the stability and governance framework of Microsoft Fabric.

  • Design and implementation of Spark workloads in Microsoft Fabric
  • Development of Python notebooks for data preparation, transformation, and analysis
  • Integration of Spark scripts into Data Factory pipelines and automation workflows
  • Creation of reusable code modules for data engineers and data scientists
  • Implementation of machine learning models using scikit-learn, PySpark MLlib, or R
  • Optimization of existing Spark processes (performance, cost, parallelization)
  • Automated data quality checks and validations using Python
  • Training and coaching for Python and Spark development in Fabric
  • Migration of existing Azure Databricks or Synapse Spark projects to Microsoft Fabric

Frequently Asked Questions on Python & Spark in Microsoft Fabric

This FAQ addresses the topics most frequently discussed in consulting engagements and training sessions. Each answer is concise and refers to additional material where appropriate. If your question is not listed, please feel free to contact us.

Comeli dragon leans against a “FAQ” sign and answers questions about python & spark in microsoft fabric.

When is it appropriate to use Spark in Microsoft Fabric?

Spark in Microsoft Fabric is particularly suitable for large, heterogeneous, or rapidly growing datasets. Typical scenarios include batch processing, streaming analytics, complex transformations, or machine learning workflows executed directly within OneLake.

How does Microsoft Fabric differ from Azure Databricks or Synapse Spark?

Microsoft Fabric integrates Spark natively into a unified platform together with OneLake, Data Factory, and Power BI. This eliminates the need for separate cluster management. Existing Databricks or Synapse Spark projects can, in many cases, be migrated to Microsoft Fabric.

Which programming languages are supported in Spark notebooks?

Microsoft Fabric notebooks support Python, R, SQL, and Scala. This enables flexible implementation of data engineering, analytics, and machine learning workflows.

How is data governance ensured in Microsoft Fabric?

Integration with Microsoft Purview enables role-based security, metadata management, and governance policies. Spark workloads can therefore be controlled and aligned with existing compliance and security frameworks.