Audience
Data Engineers, Data Architects, BI Engineers, Analytics Engineers, Heads of Data, BI Leads, Microsoft Data Platform Teams
About AnalyticsCreator
AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack.
At its core is the AnalyticsCreator Governed Control Model, which keeps business meaning, data structures, transformation rules, dependencies and technical implementation connected in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design.
Generated assets can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns.
Changes made in the model can be propagated across dependent project assets, while lineage, documentation and impact analysis remain connected to the underlying design. This helps teams reduce repetitive engineering work, standardise delivery and understand the effect of change before regenerating affected assets.
AnalyticsCreator generates native Microsoft technology rather than requiring a proprietary runtime. Organisations retain ownership of the resulting implementation and can integrate generated assets into existing Git, Azure DevOps and CI/CD processes.
Design Intelligence extends this governed project context into AI-assisted data engineering by giving authorised AI tools and agents structured access to metadata, lineage, dependencies and design rules.
Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery, SAP-to-Microsoft analytics architectures and repeatable data product engineering.
Pricing
Integrations
Videos and Screen Captures
Product Details
AnalyticsCreator Frequently Asked Questions
Q: How is AnalyticsCreator different from other data warehouse automation tools?
Q: What is the AnalyticsCreator Governed Control Model?
Q: Can AnalyticsCreator help with Microsoft Fabric and OneLake projects?
Q: How does AnalyticsCreator support AI-assisted data engineering?
Q: Can AnalyticsCreator help reduce manual SQL coding for my data team?
Q: Can AnalyticsCreator generate Power BI datasets automatically?
Q: How does AnalyticsCreator support data governance and change control?
Q: Does AnalyticsCreator replace my existing data tools or work alongside them?
Q: Does AnalyticsCreator support modern DataOps and CI/CD practices?
Q: How can I deploy AnalyticsCreator in my environment?
Q: Can I still use custom SQL and adapt generated logic?
Q: What makes AnalyticsCreator suitable for large-scale enterprise data projects?
Q: What kinds of users and organization types does AnalyticsCreator work with?
Q: What languages does AnalyticsCreator support in their product?
Q: What kind of support options does AnalyticsCreator offer?
Q: What other applications or services does AnalyticsCreator integrate with?
Q: What type of training does AnalyticsCreator provide?
Q: Does AnalyticsCreator offer a free trial?
Q: What pricing for support is available for AnalyticsCreator?
Q: What pricing for training is available for AnalyticsCreator?
AnalyticsCreator Product Features
Data Engineering
AnalyticsCreator is a model-driven data engineering application for teams working with Microsoft SQL Server, Azure and Microsoft Fabric. Engineers use source metadata and intelligent wizards to develop business and technical models, data transformations, historisation rules and processing dependencies. The corresponding implementation code evolves with the model and remains visible during development. From the governed project, AnalyticsCreator generates native database structures, SSIS packages, Azure Data Factory and supported Fabric pipeline artefacts, deployment packages and Power BI semantic-model definitions. Integrated testing and project versioning support controlled changes and repeatable delivery. Generated implementations operate within the customer's Microsoft environment without an AnalyticsCreator production runtime.
Data Integration
AnalyticsCreator supports metadata-driven data integration for Microsoft data warehouse and analytics environments. Source-system metadata is imported into a governed project where developers define relationships, mappings, ingestion rules, transformations, historisation and processing dependencies. AnalyticsCreator develops the corresponding implementation logic and generates supported SQL processing, SSIS packages, Azure Data Factory pipelines and Microsoft Fabric orchestration artefacts. When source structures change, metadata refresh and connected lineage help engineers identify affected areas before revising the model and regenerating the relevant outputs. Data movement and processing are executed by the configured target technologies, while AnalyticsCreator retains the design, generation and change-management context.
Data Lake
AnalyticsCreator supports Microsoft Fabric data architectures that use OneLake as their underlying storage foundation. Its primary role is to develop governed data warehouse models, transformation logic, processing dependencies and analytical structures, then generate supported native Microsoft Fabric Warehouse and Data Factory pipeline artefacts. The project maintains source metadata, relationships, transformations and analytical definitions so that the warehouse design can evolve while preserving its business and technical context. Microsoft Fabric provides OneLake storage, warehouse execution and pipeline runtime services. AnalyticsCreator supplies the model-driven design and generation capabilities rather than acting as a standalone data lake storage system or Spark processing engine.
Data Lineage
AnalyticsCreator provides metadata-based data lineage and dependency visibility across the data warehouse and analytical models maintained in its project. Source structures, business definitions, mappings, transformations, relationships and semantic-model definitions remain connected, helping teams understand how data moves from source systems to analytical outputs. When source metadata is refreshed, affected locations in the project lineage are highlighted for review. This supports change-impact analysis before modifying transformations, warehouse structures or downstream analytical models. Project versioning, generated documentation and recorded test results provide supporting evidence for controlled changes. Lineage reflects the recorded project design and its supported coverage, rather than universal runtime monitoring.
Data Management
AnalyticsCreator is a model-driven design and automation application for Microsoft data environments. It connects business definitions, source metadata, logical and physical models, transformation rules, implementation code, dependencies and documentation within a governed project. This connected foundation helps organisations retain knowledge of their data architecture, review the impact of changes and reduce reliance on individual developers or disconnected documentation. Project versioning, metadata-based lineage and integrated testing support controlled development and maintenance. AnalyticsCreator generates native warehouse, pipeline and analytical artefacts for supported Microsoft technologies, while the resulting solutions operate independently in the target environment. Its focus is the governance and engineering of data analytics solutions, rather than operational master data management.
Data Modeling
AnalyticsCreator enables model-first development of business, logical, physical and semantic data models for Microsoft analytics environments. Intelligent wizards use source metadata and project-specific requirements to develop dimensional, normalised, hybrid and layered warehouse architectures. Teams can model facts, dimensions, relationships, business keys, transformations and historisation rules. Unlike modelling tools that only produce diagrams, AnalyticsCreator develops the corresponding implementation code alongside the model, making it visible throughout development. The governed project connects business meaning, technical structures, transformation logic, lineage and documentation. From this foundation, AnalyticsCreator generates supported SQL Server, Azure, Microsoft Fabric and Power BI implementation artefacts. Model First. Then Generate.
Data Warehouse
AnalyticsCreator supports model-driven development and automation of data warehouses for Microsoft SQL Server, Azure SQL and supported Microsoft Fabric Warehouse environments. Intelligent wizards use source metadata and design decisions to develop project-specific warehouse models. Business definitions, relationships, transformation logic and implementation code remain connected throughout development. AnalyticsCreator generates native SQL structures, views, stored procedures, historisation and snapshot logic, DACPAC deployment packages and compatible loading and orchestration artefacts. Integrated testing compares actual processing results with defined expectations, supporting validation before release. Generated solutions run in the selected Microsoft environment without requiring an AnalyticsCreator production runtime.
ETL
AnalyticsCreator provides metadata-driven design and generation of ETL and ELT processes for Microsoft data warehouse environments. Developers define ingestion, transformation, historisation, persistence and processing dependencies within a governed project. The corresponding SQL and processing logic develops alongside the model and remains visible for review. AnalyticsCreator generates supported SQL Server Integration Services (SSIS) packages, Azure Data Factory pipelines and Microsoft Fabric pipeline artefacts, together with the required database processing logic. Integrated testing supports repeatable validation against developer-defined input and expected results. The generated workloads execute in the selected Microsoft environment rather than through a proprietary AnalyticsCreator runtime.
Metadata Management
AnalyticsCreator maintains the connected metadata used to design, develop and evolve Microsoft data analytics solutions. Source definitions, business concepts, models, relationships, transformation rules, implementation code, dependencies and deployment settings are maintained within a governed project. This metadata supports model-driven development, documentation generation, lineage analysis, change-impact review, project versioning and native implementation generation. AnalyticsCreator Design Intelligence extends this approach by making supported saved-project context accessible to authorised MCP-compatible AI agents for analysis, explanation and technical proposals. Available context depends on the project and supported coverage, while proposed changes remain subject to human review and organisational controls.
Semantic Layer
AnalyticsCreator connects semantic-model development to the governed data warehouse design, helping teams maintain consistent analytical structures across Microsoft data environments. Facts, dimensions, relationships, measures and hierarchies are defined within the same project context as the underlying source metadata and transformation logic. AnalyticsCreator generates supported Power BI semantic-model artefacts, including Power BI Project (PBIP) output with native Tabular Model Definition Language (TMDL). These text-based definitions can be reviewed, versioned and incorporated into configured Git and CI/CD workflows. Power BI remains responsible for report visuals, workspace configuration and analytical execution. AnalyticsCreator provides the connected design and generation foundation for the semantic model.
AnalyticsCreator Additional Categories
Data Lakehouse
AnalyticsCreator supports model-driven data engineering for Microsoft Fabric architectures that combine lakehouse data, data warehousing and analytical models. Using a governed metadata project, teams define data structures, relationships, transformations, historisation rules and processing dependencies. Business and technical definitions remain connected to implementation logic, lineage and documentation throughout development. AnalyticsCreator generates supported native Microsoft Fabric Warehouse SQL objects, Fabric Data Factory pipelines and Power BI semantic-model artefacts. These can form part of architectures combining OneLake storage, Fabric Lakehouse SQL analytics endpoints and Fabric Warehouse. Metadata-driven generation, version control and change-impact analysis help teams maintain consistent analytical structures as requirements evolve. AnalyticsCreator provides the design, automation and governance capabilities, while Microsoft Fabric manages storage, data proc
Data Warehouse Automation
AnalyticsCreator automates the development and ongoing evolution of Microsoft data warehouses through a governed, model-driven approach. Teams import source metadata, use intelligent wizards to develop models and transformation logic, and generate native SQL structures, historisation and snapshot processing, SSIS and Azure Data Factory pipelines, supported Microsoft Fabric artefacts, deployment packages, documentation and Power BI semantic-model definitions. Implementation code develops alongside the model, making technical changes visible during development. Integrated testing supports repeatable validation using defined input data and expected results, with differences recorded for review. Project versioning and optional Git workflows support controlled releases. Generated solutions run independently within the selected Microsoft technology environment, without requiring an AnalyticsCreator production runtime.