Azure

AnalyticsCreator supports Microsoft Azure data warehouse implementations by generating SQL-based warehouse structures, Azure Data Factory orchestration artifacts and, where configured, analytical models from the AnalyticsCreator metadata design.

AnalyticsCreator is the design and generation layer. The generated implementation runs on the selected Azure services. Azure Data Factory handles orchestration, while the configured SQL engine provides database processing and storage.

Overview

AnalyticsCreator separates the logical data warehouse design from its Azure implementation. Source structures, transformations, historization rules, dependencies and analytical definitions are maintained in the AnalyticsCreator project and used to generate the corresponding technical artifacts.

For Azure implementations, this can include database structures and procedures for supported SQL targets together with Azure Data Factory pipelines for loading and workflow orchestration.

Supported Azure Components

AnalyticsCreator can be used with Azure architectures that include:

Azure Data Factory for pipeline orchestration and data movement
Azure SQL Database as a SQL-based data warehouse target
Azure SQL Managed Instance for SQL Server-compatible Azure database environments
Azure Synapse Analytics dedicated SQL pools where this target is used
Azure Storage as a source or landing/staging component where required by the implementation
Power BI as an analytical and reporting layer

The Azure services required for a project depend on the selected architecture. AnalyticsCreator does not require every Azure implementation to use all of these components.

What AnalyticsCreator Generates

Depending on the project design and selected deployment configuration, AnalyticsCreator can generate:

SQL database structures such as tables and views
Stored procedures for generated loading, historization and transformation logic
DACPAC or other configured SQL deployment artifacts
Azure Data Factory pipeline definitions
Azure Data Factory deployment resources packaged as an ARM template
Workflow and dependency orchestration
Analytical or semantic-model artifacts where configured

The generated artifacts are derived from the metadata maintained in AnalyticsCreator and can be regenerated when the underlying design changes.

Azure Data Factory

Azure Data Factory provides the orchestration layer for AnalyticsCreator-generated Azure workflows.

AnalyticsCreator can generate an Azure Data Factory deployment package containing the required pipeline resources. The generated pipelines coordinate the configured loading and processing steps and their dependencies.

A typical generated workflow can coordinate activities such as:

source extraction and import
historization where configured
execution of generated transformation or persisting logic
dependency-aware processing of downstream objects

The exact workflow depends on the model. These stages describe common generated patterns rather than a mandatory Azure architecture for every project.

ARM Template and Environment Configuration

AnalyticsCreator can generate an ARM template for deploying the configured Azure Data Factory resources.

Generation of the template does not remove the need for Azure environment configuration. After deployment, environment-specific settings still need to be validated and configured as required.

This can include:

linked-service connections
Integration Runtime assignment
authentication and credentials
network access
source and target database names
environment-specific parameters

AnalyticsCreator's generated definitions provide the orchestration structure. Azure remains responsible for executing the deployed pipelines and for the runtime configuration required to access the relevant systems.

SQL Processing and Storage

Azure Data Factory orchestrates workflows but does not replace the SQL engine used for the warehouse itself.

For SQL-based Azure implementations, generated database structures and processing logic are deployed to the selected supported SQL target, such as Azure SQL Database, Azure SQL Managed Instance or, where configured, an Azure Synapse dedicated SQL pool.

The SQL engine then executes the generated database logic. AnalyticsCreator itself is not a production data-processing runtime.

Modeling and Warehouse Design

The logical warehouse design is defined in AnalyticsCreator metadata rather than being dictated by Azure.

Depending on the project, AnalyticsCreator can support designs including:

dimensional models with facts and dimensions
normalized data warehouse structures
historized structures and Slowly Changing Dimensions
Data Vault and hybrid modeling approaches where required
data marts and analytical structures

Individual AnalyticsCreator projects may use staging, persistent historization, transformation and data-mart layers. The exact topology depends on the selected design and should not be treated as an Azure platform requirement.

Generation, Deployment and Execution

Generation, deployment and execution are separate activities.

Generation: AnalyticsCreator converts the metadata design into SQL, database deployment artifacts, Azure Data Factory definitions and optional analytical artifacts.
Deployment: The generated artifacts are applied or deployed to the corresponding Azure resources using the configured deployment process.
Execution: Azure Data Factory and the selected SQL services execute the deployed workflows and database logic.

Generating or deploying a pipeline does not mean that business data has already been processed. Data movement and transformation occur when the deployed workflow is executed.

Typical Azure Workflow

Design the solution in AnalyticsCreator. Import source metadata and define the required warehouse structures, transformations, historization rules and dependencies.
Refine and validate the model. Review generated structures and apply the required business logic before deployment.
Generate the SQL implementation. Create the configured database structures, procedures and deployment artifacts for the selected Azure SQL target.
Generate the Azure Data Factory deployment package. AnalyticsCreator creates the configured ADF pipeline resources and ARM template.
Deploy and configure the Azure environment. Deploy the database and Data Factory artifacts and configure linked services, Integration Runtimes, credentials and environment-specific settings.
Execute and validate. Run the Azure Data Factory workflow and validate the resulting data, dependencies and downstream analytical structures.

CI/CD and Version Control

AnalyticsCreator separates the maintained metadata design from the generated Azure implementation. Project definitions and generated deployment artifacts can therefore be incorporated into controlled source-control and CI/CD processes.

Teams can use this approach to:

review model changes before generation and deployment
version AnalyticsCreator project definitions
review generated technical changes
promote approved deployment artifacts between environments
retain an auditable history of implementation changes

Source control, artifact generation, Azure deployment and pipeline execution remain separate steps and should be managed accordingly.

Sources and Connectors

Azure Data Factory can participate in the execution of workflows involving cloud and on-premises sources. The source systems used by an AnalyticsCreator project are defined through the appropriate AnalyticsCreator connectors and deployment configuration.

Refer to the Connector documentation for the supported AnalyticsCreator connector types and their individual configuration requirements.

Prerequisites and Considerations

The required Azure resources must exist or be provisioned as part of the implementation.
The target SQL environment must be supported and correctly configured.
Azure Data Factory linked services and Integration Runtimes must be configured for the relevant source and target systems.
Authentication, networking and permissions remain environment-specific Azure responsibilities.
Generated SQL must be compatible with the selected SQL target.
Generated deployment artifacts should be reviewed before promotion between environments.
Pipeline execution and resulting data should be validated after deployment.

Key Takeaway

AnalyticsCreator defines and generates the Azure data warehouse implementation from governed metadata. It creates the configured SQL and Azure Data Factory artifacts, while Azure provides the database, orchestration and execution environment. The generated implementation remains native to the selected Microsoft technologies and can operate independently of an AnalyticsCreator runtime.