Azure

This page describes how AnalyticsCreator generates and integrates data warehouse solutions in Microsoft Azure environments, with a focus on Azure Data Factory for orchestration and SQL-based engines for processing.

Overview

AnalyticsCreator supports Microsoft Azure as a target environment by generating SQL-based data warehouse structures, orchestration pipelines, and analytical models. Azure Data Factory is used for workflow orchestration, while SQL-based engines handle data processing and storage.

AnalyticsCreator generates all required artifacts, but execution is performed by Azure services such as Data Factory and SQL engines.

Supported Services and Components

  • Azure Data Factory (orchestration)
  • Azure SQL Database
  • Azure SQL Managed Instance
  • Azure Synapse Analytics (SQL pools)
  • Azure Storage (as data source or staging area)
  • Power BI (analytical layer)

What AnalyticsCreator Generates

For Azure environments, AnalyticsCreator generates:

  • SQL objects:
    • STG tables (import layer)
    • Persistent staging and historization tables
    • CORE transformations (views or persisted tables)
    • DM layer (facts and dimensions)
  • Stored procedures for:
    • Data loading
    • Historization
    • Persisting logic
  • Azure Data Factory pipelines:
    • Execution orchestration
    • Dependency handling
    • Integration with linked services
  • Semantic models for reporting tools such as Power BI

Supported Modeling Approaches

  • Dimensional modeling (facts and dimensions)
  • Data Vault modeling (hubs, links, satellites)
  • Hybrid approaches
  • Historized models (SCD2 with valid-from and valid-to)

Modeling behavior is independent of Azure and is defined in metadata. Azure determines where and how generated logic is executed.

Deployment and Execution Model

AnalyticsCreator separates generation, deployment, and execution:

  • AnalyticsCreator generates SQL objects and pipeline definitions
  • Deployment publishes these artifacts to Azure services
  • Execution is handled by Azure Data Factory and SQL engines

Typical execution flow:

  • Azure Data Factory triggers pipelines
  • Data is extracted from sources
  • Data is written to STG tables
  • Stored procedures execute transformations and historization
  • CORE and DM layers are updated

CI/CD and Version Control

  • Metadata is stored in the AnalyticsCreator repository
  • Projects can be versioned via JSON export (acrepo)
  • Generated artifacts can be integrated into Azure DevOps pipelines
  • Deployment configurations support multiple environments

Connectors, Sources, and Exports

Supported sources

  • SAP systems
  • SQL Server and Azure SQL
  • Flat files and external storage via Data Factory

Exports and targets

  • Azure SQL Database
  • Azure Synapse SQL pools
  • Power BI semantic models

Prerequisites, Limitations, and Notes

  • Azure subscription and resource group required
  • Data Factory instance must be configured
  • Linked services must be defined for source systems
  • SQL compatibility depends on target engine (Azure SQL vs Synapse)

Design considerations:

  • Azure environments are modular and require explicit configuration
  • Orchestration and storage are separated services
  • Performance depends on selected SQL engine and scaling configuration

Example Use Cases

  • Building a cloud-based data warehouse using Azure SQL Database
  • Using Azure Data Factory to orchestrate ETL pipelines
  • Implementing Data Vault models in Azure Synapse
  • Automating pipeline generation instead of manual ADF development

Platform-specific Notes

  • Azure separates orchestration (Data Factory) from compute (SQL engines)
  • Pipeline configuration requires linked services and integration runtimes
  • Multiple SQL engines can be used depending on workload requirements

Related Content

Key Takeaway

AnalyticsCreator generates SQL structures and Azure Data Factory pipelines, while execution is handled by Azure services such as Data Factory and SQL-based compute engines.