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

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.