C#  

Advanced Data Warehouse Modeling and Querying Using C# and .NET 9

Data warehouses are there to provide analytics, reporting, and business intelligence. Though the majority of the attention ultimately lands on SQL and ETL tools, C# can be a valuable addition to warehouse schema design, metadata management, model definition, and querying at scale. In this article, we're covering data warehousing using C# 14 and .NET 9 in an expert way.

Why C# Data Warehousing?

  • Automate the creation of warehouse schema (fact/dimension tables).
  • Regulating and certifying source-to-target mapping models.
  • Construct models dimensionally automatically.
  • Generate surrogate keys, SCD Type 2 rows.
  • Integrate with Azure Synapse, Snowflake, and BigQuery APIs.
  • Execute high-performance warehouse queries through parameterization and batching.

C# is well adapted to metadata-programming, code generation, and system integration — all of which are central to data warehouse maintainability.

Programmatic Warehouse Modeling in C#

Let's create a straightforward dimensional model in present-day C#.

public record CustomerDim(string CustomerKey, string CustomerName, string Country);
public record OrderFact(
    string OrderKey,
    string CustomerKey,
    DateTime OrderDate,
    decimal TotalAmount,
    string CurrencyCode);

You can cast the source data to those types before loading, or even generate the equivalent SQL CREATE TABLE scripts from attributes.

[WarehouseTable("dw.CustomerDim")] 
public record CustomerDim(string CustomerKey, string CustomerName, string Country);

Use source generation or introspection to obtain the DDL based on annotated classes.

Warehouse Querying from C#

Instead of running raw SQL, put parameterized warehouse queries inside reusable procedures.

public async Task<List<OrderFact>> GetSalesByDateAsync(DateTime from, DateTime to)
{
    const string sql = @"
        SELECT 
            OrderKey, 
            CustomerKey, 
            OrderDate, 
            TotalAmount, 
            CurrencyCode 
        FROM dw.OrderFact 
        WHERE OrderDate BETWEEN @from AND @to";
    using var conn = new SqlConnection(_warehouseConn);
    var results = await conn.QueryAsync<OrderFact>(sql, new { from, to });
    return results.ToList();
}

This design pattern allows you to.

  • Develop C# console applications for analytics APIs to display dashboards or reports
  • Export to Excel, Power BI, CSV, or PDF
  • Run batch summaries or ML feature generation jobs
  • High-Level Features
  • Surrogate Key Generation

C# can handle surrogate keys either in-process or through sequences.

int nextKey = await conn.ExecuteScalarAsync<int>(
    "SELECT NEXT VALUE FOR dw.CustomerKeySeq"
);

Slowly Changing Dimensions (SCD Type 2)

Use EF Core or Dapper to insert new rows for updated attributes with validity ranges.

if (existing.Name != updated.Name)
{
    // End old record
    existing.EndDate = DateTime.UtcNow;

    // Add new record
    var newVersion = new CustomerDim
    {
        // Assign necessary properties here
    };
    await conn.ExecuteAsync("INSERT INTO dw.CustomerDim ...", newVersion);
}

Query Materialization

Use the ToDataTable() extension methods to convert warehouse queries into in-memory tables.

var table = queryResults.ToDataTable();
ExportToCsv(table, "output/sales.csv");

BI Tool and API Integration

C# can,

  • Feed Power BI through REST or tabular model APIs
  • Push metrics to dashboards
  • Develop REST APIs that wrap SQL with business-oriented endpoints

Conclusion

Automate report sharing via email, Teams, or Slack. Conclusion With .NET 9 and C# 14, you can be hands-on and flexible in data warehouse modeling and querying. Whether modeling dimensions, building APIs, or filling dashboards, C# gives you control, performance, and maintainability that you simply can't get with SQL scripts alone.

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