The purpose of data warehouses is to offer business intelligence, analytics, and reporting. Even while SQL and ETL technologies receive most of the focus, C# can be a useful complement to model definition, metadata management, warehouse schema design, and large-scale querying. This article provides expert coverage of data warehousing with C# 14 and.NET 9.

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.

Data warehouse maintainability relies heavily on metadata-programming, code generation, and system integration, all of which C# excels at.

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.