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Develop the model and code. Generate native assets.

Teams develop their data analytics solution in AnalyticsCreator using intelligent wizards and connected business, logical, physical and semantic models. The corresponding code develops with the model and remains visible throughout development. From this connected project, AnalyticsCreator generates native implementation artefacts and deployment packages for the selected environment.

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Develop with visible code. Generate native assets. Deploy with control.

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The Design Control Plane

Keep design and implementation connected.

AnalyticsCreator acts as the design-time control plane where teams develop connected models and their implementation code. Business definitions, relationships, transformations, semantic models and target settings remain part of the same governed project.

Native assets and deployment packages are generated from this project. The resulting solution runs in its target environment without requiring AnalyticsCreator as a production runtime. Operation can be managed by the customer, a partner or both.

Design Intelligence makes available saved-project context accessible to authorised AI agents through MCP. Agents can use this context for analysis and technical assistance, with proposed project changes subject to review and the organisation's change controls.

Predictable Generation

Support decisions with AI. Keep implementation governed.

AI-supported work

AI can interpret project context, explain dependencies, propose changes and generate code. Its suggestions require assessment against the project's requirements and design.

AnalyticsCreator's deterministic generation

AnalyticsCreator applies defined rules as teams develop the model and its code. Native assets are generated from the governed project and selected target settings, providing reproducible outputs from the same project state, rules and settings.

Design Intelligence provides available project knowledge for AI-assisted work. Teams assess proposed changes within the connected model and code before generating the native assets for deployment.

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Peter Smoly, CEO of Analytics Creator explains how to build a modern data warehouse new and changing requirements from business and IT result in a dificiency of your data architecture.

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Generated Outputs

Generate the code and artefacts your target environment requires.

The generated outputs depend on the architecture and target settings selected for the project. AnalyticsCreator supports on-premises SQL Server, Azure and Microsoft Fabric, alongside analytical outputs for Power BI, Tableau and Qlik.

Database implementation
SQL Server objects, stored procedures and DACPAC deployment packages.
Data processing
SSIS packages, Azure Data Factory pipelines and supported Microsoft Fabric pipelines and warehouse artefacts.
Analytical models and scripts
Power BI projects (PBIP), Tableau models and Qlik scripts.
OLAP models
Tabular OLAP and Multidimensional OLAP. Generated tabular semantic models provide structured definitions for analytics and AI use.
Supporting project artefacts
Documentation, lineage information and deployment packages.
From Design to Operation

Develop the solution. Prepare its native implementation.

The AnalyticsCreator project connects source metadata, business definitions, data and semantic models, transformation logic and code throughout development and change.

  1. Connect

    Collect source metadata as the starting point for the project's models and processing logic.

  2. Develop

    Develop project-specific models with intelligent wizards. See the corresponding code evolve as you work.

  3. Generate native assets

    Prepare native implementation artefacts and deployment packages for the selected target environment.

  4. Deploy and operate

    Run the solution in the selected environment, without an AnalyticsCreator production runtime.

Integrated validation and regression testing support development and subsequent changes. Test reports help teams identify unintended effects before release.

Project Versioning

Preserve the model and its implementation together.

AnalyticsCreator's integrated project versioning lets teams save and restore defined project states containing the connected models, transformation logic, code and settings.

This preserves the development context behind each version and provides a foundation for continued work and controlled changes. Optional Git integration supports existing comparison and review processes.

Connected Changes

Understand the impact. Develop the change.

When source metadata is refreshed, AnalyticsCreator automatically shows affected places in the connected lineage. Teams can see which structures and transformations need attention.

Developers update the project using intelligent wizards and modelling tools. The corresponding code evolves with the model and remains visible as they work. They can then generate the affected native assets from the updated project and validate the changes before release.

CI/CD Integration

Fit native artefacts into your release process.

Git and CI/CD are optional. Teams can use AnalyticsCreator's integrated capabilities for project versioning, testing, native asset generation, packaging and deployment.

Where an organisation uses GitHub or Azure DevOps, the AnalyticsCreator project and generated deployment artefacts can be incorporated into its existing comparison, review and release processes. Teams retain their approval steps and control how releases move through development, test and production environments.

Validation and Regression Testing

Validate changes. Identify unintended effects.

AnalyticsCreator's integrated data analytics testing feature executes validations, compares results with expected outcomes and records differences in test reports.

Regression testing helps teams identify unintended effects after model or transformation changes. Automated execution and comparison reduce testing effort and support faster, more reliable release cycles.

Code and artefact generation: common questions

What code and artefacts can AnalyticsCreator generate?

AnalyticsCreator generates database objects and DACPAC packages, SSIS packages, Azure Data Factory pipelines, supported Microsoft Fabric artefacts, Power BI projects (PBIP), Tableau models, Qlik scripts, Tabular OLAP and Multidimensional OLAP models. Outputs depend on the selected project architecture and target settings.

Can generated assets run without AnalyticsCreator?

Yes. Generated assets run in their target environment without an AnalyticsCreator production runtime. The customer, a partner or both can manage operation. AnalyticsCreator is used for continued model-driven development and for generating updated native assets.

Do we need GitHub or Azure DevOps?

No. AnalyticsCreator includes project versioning, code and artefact generation, packaging and deployment capabilities. GitHub or Azure DevOps can be integrated when an organisation wants to incorporate the project and generated artefacts into its existing review and release processes.

How does testing in AnalyticsCreator work?

AnalyticsCreator’s integrated testing feature executes validations, compares results with expected outcomes and records differences in test reports. Regression testing helps identify unintended effects after model or transformation changes, reducing manual checking and supporting release decisions.

When does AnalyticsCreator create the implementation code?

The corresponding implementation code develops as teams build and change the model in AnalyticsCreator. Developers can inspect that code throughout development. Native asset generation then prepares the implementation artefacts and deployment packages required by the selected target environment.