AnalyticsCreator Documentation
Design the model. Generate the technology.
Learn how to design, generate, deploy, and maintain governed data solutions with AnalyticsCreator across SQL Server, Azure Data Factory, Microsoft Fabric, Power BI, and the wider Microsoft data stack.
Build from a governed design
AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering. Define structures, mappings, transformations, historisation, and analytical models in the project, then generate native artefacts for supported Microsoft technologies.
Use this documentation to learn the application, configure projects and connectors, work with data models and transformations, generate target artefacts, and manage delivery from development through deployment.
Documentation
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Find guidance for setup, modelling, generation, platform support, day-to-day development, and technical reference.
User Guide
Browse the User Guide for setup, the desktop interface, working with AnalyticsCreator, advanced features and wizards. Select a card to open its subsection or overview. Getting Started Quick Start Guide→ Build your first Data Warehouse with Northwind, from SQL Server preparation to generated loading files and results. Understanding AnalyticsCreator→ Understand how repository metadata drives warehouse modeling, generation, deployment and execution. System Requirements→ Review operating system, network and SQL Server requirements before installing AnalyticsCreator. Configuring AnalyticsCreator→ Configure repository storage and SQL Server authentication, with optional backup paths and proxy settings. Desktop Interface Desktop Interface→ Find editing, navigation and project management features, with links to interface settings and project save/load guides. Working with AnalyticsCreator Using DWH Wizard→ Read source metadata or reuse stored Source definitions, then review the warehouse objects to generate. Connectors→ Configure connection settings for your source technology, then check and save the Connector definition. Historization→ Create historization definitions and review column policies, filters, variables and scripts for the required record history. Transformations→ Create Regular or Manual transformations and configure expressions, dimensions, snapshots and datamart assignments. Persisting→ Create a persisting definition, add its content and choose the replacement behavior required for the target data. Export→ Create export definitions and review column mappings, target clearing, filters and scripts before execution. Layers and Schemas→ Create schemas, assign them to warehouse layers and review layer sequence numbers and saved assignments. Partitions→ Create and maintain OLAP partitions, reviewing their analytical tables, configuration and saved settings before downstream processing. Macros→ Define reusable Macros, specify expressions and arguments, and check calls between Macros. Scripts→ Create lifecycle scripts and prepare the configuration for a Script transformation example. Hierarchies→ Create an OLAP hierarchy and review its analytical table, columns and level order. Indexes→ Configure table indexes and review the table names, columns, primary keys and Identity settings that support them. Table and Column Properties→ Add calculated column expressions and inspect column dependencies before changing definitions or downstream use. Datamart→ Configure datamart measures, friendly names, OLAP export settings, table references and DAX calculated columns. Object Groups→ Create and maintain named object groups, then assign project objects and review group membership. Deployment→ Configure and save a deployment package definition for the intended target environment before running a deployment. Interface→ Find objects, apply and reuse diagram filters, and adjust the interface scale with Ctrl+Mousewheel. User Groups→ Create user groups, maintain member assignments and verify the saved membership and rights. Save and Load→ Save and load project folders, work with Git, or use AnalyticsCreator cloud storage for saved repositories. Interface Appearance→ Adjust diagram, navigation tree, page and color settings, then check their effect on the interface. DWH Settings→ Review and change common service-field names, then check project definitions affected by the naming changes. Source References→ Define relationships between Sources by selecting the related Sources, mapping their columns and saving the reference. Data Vault→ Create Data Vault definitions with DWH Wizard or Vault Wizard, and add or refresh hash keys. Meta-Connector→ Create or load Source definitions from connector metadata, then select and classify them in DWH Wizard. Sources→ Create table or query Sources, or configure an existing CSV or Excel Source to use matching files. Import→ Create import definitions and packages, then configure field mappings, row filters, variables and scripts before execution. Encrypted Strings→ Create encrypted-string entries and use their aliases in connector connection strings, then test and save the connection. Parameters→ Change a parameter’s Custom value and verify the saved setting for the workflows that use it. Table References→ Create or maintain table references by reviewing table endpoints, matching conditions, relationship settings and affected transformations. Test cases→ Run a configured test case, compare actual output with expected data, and inspect differences and logs. Advanced Features Advanced Features→ Explore scripts, historization, parameters and Macros for customizing warehouse behavior. Wizards DWH Wizard→ Create warehouse definitions from source metadata, using a Connector or existing Source definitions. Snapshot transformation wizard→ Create a snapshot dimension and review its schema, name and datamart assignments. Import wizard→ Define an import by selecting the source, target table and package settings. Source Wizard→ Add a Source definition from a table or query and review its metadata settings. Time transformation wizard→ Create a time dimension with a chosen interval, naming settings and time-to-key mapping. Historization wizard→ Choose source, target, package and history settings for a new historization definition. Persisting wizard→ Create a definition to store a transformation result in a table, with target and package settings. Transformation wizard→ Create a Transformation definition by choosing its type, input tables and configuration settings. Calendar transformation wizard→ Create a calendar dimension with a date range, naming settings and datamart assignments.
Explore categoryReference
This section provides structured technical reference documentation for AnalyticsCreator. It is intended for users who need detailed information about the user interface, entity types, entities, and configuration parameters. Use this section when you already know which part of the application you want to understand and need a precise description of available objects, categories, and options. Reference sections Entities Reference for the concrete entities used in AnalyticsCreator and their roles in modeling, generation, and execution. Modeling objects Execution-related entities Generated object definitions Open Entities Entity Types Reference for the structural categories used in AnalyticsCreator, such as connectors, sources, tables, transformations, packages, scripts, and schemas. Connector and source categories Table and transformation types Schema, package, and script types Open Entity types Parameters Reference for configuration parameters and settings that control generation, execution, historization, and other system behavior. Object-specific parameters Execution and generation settings Configuration options Open Parameters User Interface Reference information for the AnalyticsCreator user interface and its structural elements. Navigation and UI components Windows, dialogs, and views Interaction patterns Open User Interface How to use this section The Reference section is organized by topic area rather than by workflow. Use User Interface when you need help locating or understanding specific interface elements Use Entity Types when you need to understand the available structural categories in AnalyticsCreator Use Entities when you need reference information about concrete objects used in the model Use Parameters when you need detailed information about settings and configurable behavior When to use Reference instead of other sections Use Getting Started for onboarding and step-by-step implementation flow Use Tutorials for guided example walkthroughs Use Reference for precise technical definitions and detailed lookup documentation Key takeaway The Reference section provides structured technical lookup documentation for AnalyticsCreator user interface elements, entity categories, concrete entities, and configuration parameters. NextUser Interface
Explore categoryTutorials
This section contains guided walkthroughs based on sample datasets and example scenarios. Tutorials are intended to help you become familiar with AnalyticsCreator by following complete modeling, generation, deployment, and execution flows in a controlled environment. Use these tutorials to understand how metadata is translated into warehouse structures, pipelines, and analytical models across different platforms and source scenarios. Available Tutorials Northwind Data Warehouse Guided walkthrough based on the Northwind dataset. Source import and modeling Transformations and data marts End-to-end warehouse flow Open Northwind DWH Walkthrough Coming soon SQL Server Data Warehouse Walkthrough End-to-end tutorial for building a SQL Server-based warehouse. Repository and connector setup Wizard-generated model Execution with SQL Server and SSIS Coming soon Microsoft Fabric Walkthrough Tutorial for generating and deploying a warehouse model to Microsoft Fabric. Fabric target setup Pipeline generation Semantic model integration Coming soon SAP to Data Warehouse Walkthrough Tutorial for importing SAP metadata and generating a layered warehouse model. SAP metadata import Persistent staging Dimensional or hybrid modeling How to Use This Section Tutorials are intended as practical implementation guides. They are most useful when followed in a test environment together with the Quick Start Guide and the related reference pages. Start with Northwind if you are new to AnalyticsCreator Use platform-specific tutorials to understand deployment patterns Use source-specific tutorials to understand metadata import and modeling behavior Common Principles Across Tutorials Practical walkthroughs Each tutorial focuses on a complete implementation flow rather than isolated features. Sample datasets Tutorials use controlled example data so that modeling and generation steps can be reproduced. End-to-end flow Tutorials typically cover metadata import, model generation, deployment, and execution. Reference alignment Tutorial steps should be used together with technical reference pages for deeper detail. Key Takeaway Tutorials provide guided, reproducible examples that show how AnalyticsCreator is used in practice across datasets, platforms, and source scenarios.
Explore categoryPlatform Support
This section describes how AnalyticsCreator works with supported Microsoft data and analytical technologies and what it generates for each environment. AnalyticsCreator turns a governed metadata-driven design into native data warehouse, orchestration, deployment, and analytical artefacts. The generated assets are deployed to and operated within the customer's Microsoft environment without requiring an AnalyticsCreator production runtime. Supported Technologies Microsoft SQL Server Generate native SQL Server data warehouse structures, processing logic, deployment artefacts, and Microsoft analytical outputs from the metadata-driven design. Tables, views, and stored procedures Historisation, persistence, and snapshot processing DACPAC generation SSIS orchestration SSAS analytical models View SQL Server support Related technologies SQL Server Integration Services (SSIS) SQL Server Analysis Services (SSAS) Microsoft Azure Support Azure-based data engineering architectures with generated Azure Data Factory orchestration alongside native data warehouse and analytical artefacts for supported Microsoft targets. Azure Data Factory integration Generated pipeline orchestration Source ingestion and processing workflows Integration with supported Microsoft targets View Azure support Related technology Azure Data Factory Microsoft Fabric Generate native warehouse, pipeline, and analytical artefacts for supported Microsoft Fabric architectures from the governed AnalyticsCreator design. Microsoft Fabric Warehouse Fabric Data Pipelines OneLake-based storage context Power BI semantic models Direct Lake scenarios where supported View Microsoft Fabric support Microsoft Power BI Generate native Power BI semantic model artefacts from the same governed analytical design used for the wider data solution. Power BI semantic models Power BI Desktop Projects (PBIP) Tabular Model Definition Language (TMDL) XMLA-based deployment Relationships, measures, and hierarchies View Power BI support Technology-specific Support Some Microsoft technologies have dedicated pages because AnalyticsCreator generates substantial technology-specific artefacts for them. Azure Data Factory Generated pipelines, datasets, linked service definitions, and dependency-based orchestration for supported Azure data engineering workflows. View ADF support SSIS Generated SSIS packages for source ingestion, data warehouse processing, historisation, persistence, and workflow orchestration. View SSIS support SSAS Generated SSAS Tabular and Multidimensional analytical models based on the governed AnalyticsCreator design. View SSAS support How to Use This Section Each page explains the role of the Microsoft technology within an AnalyticsCreator-generated solution and distinguishes between what AnalyticsCreator designs and generates and what the target technology executes. The pages cover: Supported services and components Artefacts generated by AnalyticsCreator Deployment and execution responsibilities CI/CD and version-control considerations Relevant connectors and source systems Prerequisites and technology-specific limitations Common implementation scenarios Common Principles Model-driven design Mappings, transformations, structures, and processing dependencies are defined through AnalyticsCreator metadata. Native Microsoft artefacts AnalyticsCreator generates native artefacts for the supported Microsoft technologies rather than introducing a proprietary production format. Target-side execution Generated workloads are executed by SQL Server, SSIS, Azure Data Factory, Microsoft Fabric, Analysis Services, Power BI, or the relevant target technology. No AnalyticsCreator runtime Deployed solutions do not require AnalyticsCreator to remain running in the production environment. Source-controlled delivery Project definitions and supported generated artefacts can participate in version-control and CI/CD processes. Technology-specific generation AnalyticsCreator generates artefacts appropriate to the selected target rather than applying the same physical implementation to every Microsoft technology. Choosing the Relevant Documentation Start with the page for the primary technology you are implementing: SQL Server for SQL Server data warehouse generation and the wider SQL Server architecture Microsoft Azure for Azure-based architectures using Azure Data Factory orchestration Microsoft Fabric for supported Fabric Warehouse, pipeline, OneLake, and analytical scenarios Power BI for semantic model, PBIP, TMDL, and XMLA generation Use the Azure Data Factory, SSIS, and SSAS pages when you need implementation details for those specific technologies. Key Takeaway AnalyticsCreator turns a governed metadata-driven design into native artefacts for supported Microsoft data and analytical technologies. The generated assets are deployed to and operated within the customer's Microsoft environment, while AnalyticsCreator remains the design and generation application rather than a production runtime.
Explore categoryHow AnalyticsCreator works
From governed model to native Microsoft artefacts
The documentation follows the same separation between design, generation, and operation used by AnalyticsCreator.
Control the model
Define source structures, mappings, transformations, warehouse models, historisation, and analytical structures through project metadata.
Generate native artefacts
Generate supported SQL Server, SSIS, Azure Data Factory, Microsoft Fabric, Power BI, and analytical model artefacts from the approved design.
Own the result
Deploy and operate the generated native artefacts in your Microsoft environment without an AnalyticsCreator production runtime.
