Microsoft Fabric Blog details strategies for accessing and managing external data in Fabric Data Warehouse, guiding data engineers and analysts through virtualization versus materialization with practical examples and trade-off analysis.

External Data Materialization Strategies in Fabric Data Warehouse

Modern analytics relies on the ability to efficiently transform raw data from diverse sources into actionable insights. Microsoft Fabric Data Warehouse supports both traditional DW tables and a variety of external file formats, including Parquet, CSV, and JSONL.

Key Decision: Virtualization vs Materialization

When dealing with external data, a central design choice is how to access and manage that data:

  • Virtualization: Access external files directly through SQL views, without ingesting data into the warehouse.
  • Materialization: Ingest the external data into warehouse tables, creating a physical copy for direct querying.

Data Virtualization using Views

  • Allows immediate access to external data sources (such as files in OneLake or ADLS) through the OPENROWSET function and SQL views.

  • Example:

    CREATE VIEW cases AS
SELECT * FROM OPENROWSET(
  BULK '/Files/bing_covid_cases/*.parquet',
  DATA_SOURCE = 'MyLakehouse'
)
  • Benefits:
    • Real-time data access (always sees the latest file contents)
    • Abstraction layer simplifies data consumption
    • Reduces redundant data movement and storage
  • Trade-offs:
    • Can be slower, especially for large datasets or complex analytics
    • Performance depends on source file format and underlying storage

Materialization for Performance

  • Copies external file contents into physical warehouse tables using CREATE TABLE AS SELECT (CTAS).

  • Example:

    CREATE TABLE cases AS
SELECT * FROM OPENROWSET(
  BULK '/Files/bing_covid_cases/*.parquet',
  DATA_SOURCE = 'MyLakehouse'
)
  • Advantages:
    • Best performance for repeated, large-scale, or complex queries
    • Warehouse-optimized data access
  • Limitations:
    • Materialized tables only reflect data as of the ingestion time. Refresh is needed for updates.

Schema Tuning and Optimization

  • For best performance, define your table schemas explicitly, especially with non-self-describing formats (CSV, JSONL).

  • Example of explicit schema definition:

    CREATE TABLE cases AS
SELECT * FROM OPENROWSET(
  BULK '/Files/bing_covid_cases/*.parquet',
  DATA_SOURCE = 'MyLakehouse'
)
WITH (
  updated DATE,
  confirmed INT,
  recovered INT,
  country VARCHAR(100)
)
  • Explicit typing prevents ambiguous mappings and optimizes queries.

Key Recommendations

  • Use virtualization when you need up-to-date data, ad hoc querying, or to avoid continuous ingestion overhead.
  • Prefer materialization for production dashboards, frequent queries, or scenarios where query performance is essential.
  • Define schema explicitly for maximum reliability and performance, especially with loosely typed sources.

Further Resources

By understanding and applying these approaches, you can architect Fabric Data Warehouse solutions that balance data freshness, performance, and cost for modern analytics workloads.

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