Azure Synapse vs Fabric: What Changed?

For years, Azure Synapse Analytics represented the pinnacle of Microsoft’s cloud data warehousing strategy. It successfully converged big data and data warehousing into a single interface, offering a unified experience for SQL, Spark, and Integration. However, as enterprise data volumes exploded and the need for real-time insights became paramount, the friction of managing underlying infrastructure—even in a PaaS model—became a bottleneck. Enterprises found themselves spending more time on "plumbing" (configuring linked services, managing integration runtimes, and tuning separate storage pools) than on data value.

The transition from Azure Synapse to Microsoft Fabric marks a fundamental shift from a Platform-as-a-Service (PaaS) mindset to a Software-as-a-Service (SaaS) architecture. While Synapse required architects to stitch together various components, Fabric introduces a unified, "all-in-one" analytics solution. This evolution is not merely a rebranding; it is a re-engineering of the data estate. By placing OneLake—a unified, logical data lake—at the center of the ecosystem, Microsoft has eliminated the silos between data engineering, data science, and business intelligence, effectively "Power BI-ifying" the entire data stack.

In the enterprise context, this change simplifies the governance and integration landscape. Fabric leverages the existing Microsoft 365 infrastructure and Microsoft Entra ID (formerly Azure AD) to provide a seamless security model. For the senior cloud architect, the focus shifts from managing compute clusters and storage accounts to orchestrating data domains and workspace capacities. This architectural pivot enables a more agile response to business requirements, ensuring that data is accessible, governed, and ready for the era of generative AI.

Architecture: From Siloed Pools to OneLake

The architectural shift is defined by the move from distinct storage and compute silos to a unified "OneLake" foundation. In Synapse, you managed dedicated SQL pools, serverless pools, and Spark pools, often requiring data movement between them. In Fabric, data is stored in a single, open format (Delta Parquet) within OneLake, and multiple specialized engines (SQL, Spark, KQL) operate on that same physical data without moving it.

Implementation: Interacting with OneLake via Python

One of the most significant changes for developers is the ability to interact with the Fabric environment using standard Azure SDKs, as OneLake is compatible with the Azure Data Lake Storage (ADLS) Gen2 APIs. This allows for a smooth transition of existing Python-based ingestion scripts into the Fabric ecosystem.

The following Python example demonstrates how an enterprise-grade ingestion script uses the azure-identity library to authenticate with Entra ID and upload a dataset to a Fabric Lakehouse.

from azure.identity import DefaultAzureCredential
from azure.storage.filedatalake import DataLakeServiceClient
import pandas as pd
import io

def upload_to_fabric_onelake(workspace_name, lakehouse_name, file_path, data_frame):
    # OneLake uses the same endpoint structure as ADLS Gen2
    onelake_endpoint = f"https://onelake.dfs.fabric.microsoft.com"
    
    # Use DefaultAzureCredential for seamless Entra ID integration
    credential = DefaultAzureCredential()
    service_client = DataLakeServiceClient(endpoint=onelake_endpoint, credential=credential)
    
    # Fabric pathing: /workspace_name/lakehouse_name.Lakehouse/Files/path
    file_system_client = service_client.get_file_system_client(workspace_name)
    directory_path = f"{lakehouse_name}.Lakehouse/Files/IngestedData"
    directory_client = file_system_client.get_directory_client(directory_path)
    
    # Convert DataFrame to Parquet for OneLake optimization
    buffer = io.BytesIO()
    data_frame.to_parquet(buffer, index=False)
    file_contents = buffer.getvalue()
    
    # Upload the file
    file_client = directory_client.create_file(file_path)
    file_client.append_data(data=file_contents, offset=0, length=len(file_contents))
    file_client.flush_data(len(file_contents))
    
    print(f"Successfully uploaded {file_path} to OneLake.")

# Usage in an enterprise pipeline
df = pd.read_csv("enterprise_sales.csv")
upload_to_fabric_onelake("Finance_Workspace", "Global_Sales_LH", "sales_data.parquet", df)

Service Comparison: Azure, AWS, and GCP

Navigating the multi-cloud landscape requires understanding how Fabric compares to equivalent offerings from other major providers. While AWS and GCP offer robust data platforms, Fabric's unique "SaaS-first" approach to a unified data lake is a differentiator.

Enterprise Integration and Workflow

Enterprise integration in the Fabric era focuses on the "Shortcut" feature and Microsoft Purview. Shortcuts allow architects to virtualize data from AWS S3 or Google Cloud Storage into OneLake without moving the bits, reducing egress costs and data duplication. Governance is applied centrally through Purview, ensuring that data lineage and sensitivity labels persist across the entire pipeline.

Cost and Governance Strategy

The shift from Synapse to Fabric introduces a unified capacity model. Instead of managing separate costs for SQL DTUs, Spark vCores, and Data Factory Orchestration units, Fabric utilizes "F-SKUs" (Fabric Capacities). These capacities are shared across all workloads in a workspace, allowing for better resource utilization. Governance is simplified through "OneSecurity," where permissions defined at the OneLake level are respected by all compute engines.

Conclusion

The evolution from Azure Synapse to Microsoft Fabric represents a significant milestone for enterprise data strategy. By abstracting the complexities of infrastructure management and providing a unified SaaS environment, Fabric allows organizations to focus on what truly matters: deriving actionable insights from their data. The introduction of OneLake as a "single source of truth," combined with the power of Direct Lake mode for Power BI, significantly reduces the time-to-value for data projects.

For the senior architect, the transition requires a shift in focus toward data domain modeling, capacity optimization, and robust governance through Microsoft Purview. While Synapse remains a powerful tool for specific PaaS-heavy requirements, Fabric is clearly the future of the Microsoft data ecosystem. Adopting Fabric is not just about using a new tool; it is about embracing a more integrated, governed, and scalable way of managing the enterprise data lifecycle.

References

• https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview
• https://learn.microsoft.com/en-us/fabric/get-started/fabric-vs-synapse
• https://azure.microsoft.com/en-us/blog/introducing-microsoft-fabric-data-analytics-for-the-era-of-ai/