Getting Started

This section provides the fastest path to understanding how to set up and use AnalyticsCreator. It focuses on how a data warehouse is generated, deployed, and executed based on metadata definitions.

If you are new to AnalyticsCreator, start with the Quick Start Guide. It walks through the full workflow from repository creation to data consumption.

Recommended Path

1. Quick Start Guide
   End-to-end implementation flow from metadata to deployed data warehouse
2. Understanding AnalyticsCreator
   Architecture, warehouse layers, and design principles
3. Installation and Configuration
   Review the system requirements, follow Install and open AnalyticsCreator, and use the configuration settings below.

Typical Workflow

1. Create a repository
2. Define or import connectors
3. Import source metadata
4. Run the data warehouse wizard
5. Refine the generated model
6. Synchronize the structure
7. Deploy artifacts
8. Execute workflows
9. Consume data through data marts and semantic models

Understanding AnalyticsCreator

AnalyticsCreator is a metadata-driven design application for building and automating data warehouses and analytical models. Instead of manually implementing ETL and SQL logic, developers define metadata such as sources, keys, relationships, transformations, and loading behavior. AnalyticsCreator uses these definitions to generate database objects, pipelines, and semantic models.

Repository and Metadata

Every AnalyticsCreator project is based on a repository. The repository is a SQL Server database that stores the full metadata definition of the data warehouse. This includes connectors, source objects, transformations, keys, relationships, deployment settings, and other object definitions. The repository is the design-time control layer and the source for all generated artifacts.

This means the target database is not modeled manually. Instead, AnalyticsCreator reads the repository metadata and generates the required SQL structures from it. Generated code can run independently after deployment because AnalyticsCreator is used as a design-time application, not as a runtime dependency.

Connectors and Metadata Import

AnalyticsCreator connects to source systems such as SQL Server or SAP and imports structural metadata including tables, columns, keys, and references. In some scenarios, metadata can also be imported through metadata connectors, which makes it possible to model a data warehouse without an active connection to the live source system during design.

Imported metadata is stored in the repository and later used by the wizard to generate the draft warehouse model. At this stage, no warehouse data has been loaded yet. Only structure and metadata are being captured.

The Wizard

The data warehouse wizard is the central acceleration mechanism in AnalyticsCreator. It analyzes source metadata and generates a draft warehouse model automatically. Depending on the selected approach, this can be a dimensional model, a Data Vault model, or a mixed approach. The wizard can create staging structures, historization layers, dimensions, facts, calendar dimensions, and default relationships based on detected metadata.

The generated model is not the end result. It is the baseline that developers refine and validate. The main engineering work happens after generation, when keys, joins, historization behavior, measures, and transformations are adjusted to fit the intended warehouse design.

Warehouse Layers

AnalyticsCreator supports a layered warehouse architecture from source to presentation. In a typical setup, this includes source objects, staging, persistent staging or historization, CORE transformations, data marts, and semantic or reporting layers. It can also generate analytical models for tools such as Power BI.

Persistent Staging

A key architectural concept is the persistent staging layer. Source data is first imported into staging structures and then stored persistently for further processing. This persistent layer is used for historization and for decoupling source extraction from downstream transformations. It allows data to be reprocessed without repeatedly reading the source system.

In dimensional scenarios, historized tables typically include surrogate keys together with valid-from and valid-to columns. In Data Vault and hybrid scenarios, additional hash-based keys and references can be generated in the staging layer as persisted calculated columns and then reused in later layers.

Transformations

Transformations in AnalyticsCreator are usually generated as SQL views based on metadata definitions. These definitions specify source tables, joins, selected columns, macros, and transformation rules. In many cases, the default generated view logic is sufficient as a starting point, but it can be refined through metadata rather than by rewriting generated SQL directly.

AnalyticsCreator also supports reusable macros for standard SQL logic, such as date-to-calendar-key conversion or hash key generation. This allows repeated logic to be defined once and reused consistently across the model.

Synchronization, Deployment, and Execution

These three steps are related but different and should not be confused.

Synchronization

Synchronization materializes the metadata model into SQL objects in the target database. This creates the database structure defined in AnalyticsCreator, such as tables, views, and procedures. It does not mean that business data has already been loaded.

Deployment

Deployment creates and distributes deployable artifacts for the selected target environment. These can include SQL database packages, SSIS packages, Azure Data Factory pipelines, and semantic models. Deployment prepares the environment but still does not imply that source data has already been processed.

Execution

Execution runs the generated workflows and pipelines. This is the step where source data is actually extracted, written to staging, historized where required, transformed into CORE structures, and exposed through data marts and semantic models. In Azure scenarios, this may happen through Azure Data Factory. In on-premise scenarios, this may happen through SSIS.

Consumption

After execution, the data warehouse can be consumed through data marts and semantic models. These structures are intended for reporting and analytics, while lower layers such as staging and historization should remain implementation layers rather than direct reporting interfaces. AnalyticsCreator can generate tabular models and structures for tools such as Power BI.

System Requirements

Before installing AnalyticsCreator, prepare the Windows computer, network access and SQL Server environment.

• Operating system: Windows 10 or later.
• Network: allow AnalyticsCreator to communicate with its service over HTTPS port 443. HTTPS encrypts the communication.
• SQL Server: prepare an accessible Microsoft SQL Server for the repository and warehouse. SQL Server on Azure Virtual Machines and Azure SQL Managed Instance are also supported data warehouse targets.

If you already have an accessible SQL Server, follow the existing-server route in Set up or choose your SQL Server, then test the connection before installing AnalyticsCreator. For the installer and its required components, follow Install and open AnalyticsCreator.

Configuring AnalyticsCreator

Configure the connection that stores your warehouse design, then review the paths and any proxy settings needed for your environment. For the complete tutorial sequence, follow Sign in and set the database location.

Application sign-in

Enter your AnalyticsCreator Login and Password in the startup Login dialog. These credentials sign in to AnalyticsCreator; they are separate from the SQL Server credentials below. For a trial account, use the credentials sent by AnalyticsCreator after your request is approved. If you need an account, use trial registration or your organization's account process.

SQL Server Settings

Use the gear button in the startup dialog to open Settings, then select SQL Server Settings. The repository stores the project's metadata definitions, not the loaded warehouse business data.

Paths and database naming

Review the fields shown in Paths. Keep the folder for AnalyticsCreator files and project backups separate from the SQL Server database-file folder.

If your dialog displays the following path fields, use each for its stated purpose:

• UNC path to store backup: a network location for project backups.
• Local SQL Server path to store backup: a location on the SQL Server computer for project backups.
• Local SQL Server path to store database: a location on the SQL Server computer for database files, separate from backup storage.

Proxy Settings

If your organization requires a proxy for the AnalyticsCreator service connection, enter the supplied connection details in Proxy settings.

Configuration walkthrough

Use this walkthrough to locate the sign-in, SQL Server, Paths and Proxy Settings controls.

The SQL Server path to store databases is the database-file folder, not a backup folder. Use the path guidance above when following the walkthrough.

Click OK to sign in. Resolve any login, connection or path error before creating the design. When the main AnalyticsCreator window opens with your prepared settings, continue with Create your warehouse design.

Related Guides

• Quick Start Guide