Welcome to AnalyticsCreator Docs

Deliver Governed Data Products at Scale

AnalyticsCreator combines data modeling, historization, CI/CD, lineage, and documentation into a single platform — enabling data teams to build, automate, and govern analytics with confidence.

Everything you need to build, automate, and ship governed data products with AnalyticsCreator — from modeling and historization to CI/CD and documentation generation.

Pro Tip:

Every page in these docs mirrors the product's UI and terminology — so you can follow along in AnalyticsCreator without switching context.

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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 Quick Start Guide End-to-end implementation flow from metadata to deployed data warehouse Understanding AnalyticsCreator Architecture, layers (STG, CORE, DM), and design principles Installation and Configuration System setup and environment configuration Typical Workflow Create repository Define connectors Run data warehouse wizard Refine model Synchronize database Deploy artifacts Execute workflows Consume data Available Sections Installation System Requirements Download and Installation Understanding AnalyticsCreator Quick Start Guide

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User Guide

You can launch AnalyticsCreator in two ways: From the desktop icon After installation or streaming setup, a desktop shortcut is created. Double-click the icon to start AnalyticsCreator. From the installer window Open the downloaded AnalyticsCreator installer. Instead of selecting Install, click Launch (labeled as Number One in the image below). A window will appear showing the available AnalyticsCreator Servers, which deliver the latest version to your system. This process launches AnalyticsCreator without performing a full installation, assuming all necessary prerequisites are already in place.

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Reference

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 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 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 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 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 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.

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Tutorials

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 tutorial 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.

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Platform Support

This section describes how AnalyticsCreator integrates with supported target platforms and what it generates for each environment. AnalyticsCreator is a metadata-driven design application that generates SQL-based data warehouse structures, orchestration artifacts, and semantic models. The generated assets are then deployed and executed on the selected target platform. Supported Platforms Microsoft Fabric Support for Fabric Data Warehouse, Lakehouse SQL endpoints, OneLake, pipelines, and integrated semantic models. Warehouse and lakehouse targets Pipeline generation Semantic model integration View platform details Microsoft Azure / Data Factory Support for Azure Data Factory orchestration together with Azure SQL and Synapse-based warehouse execution. ADF pipeline generation Azure SQL and Synapse targets Cloud orchestration View platform details SQL Server Support for SQL Server-based repositories, warehouse generation, and SSIS-based execution. On-premise warehouse targets SQL and stored procedure generation SSIS orchestration View platform details Power BI Support for semantic model generation including measures, relationships, and analytical structures. Tabular models Measures and relationships Reporting layer View platform details How to Use This Section Each platform page explains how AnalyticsCreator maps metadata definitions to platform-specific implementations. Supported services and runtimes Generated SQL, pipelines, and semantic models Deployment and execution behavior Platform-specific constraints and design considerations Common Principles Across Platforms Metadata-driven generation All structures and logic are generated from metadata definitions. Platform-side execution Processing and orchestration run on the target platform. Consistent modeling approach Dimensional, Data Vault, and hybrid models are supported across platforms. Generated deployment assets SQL objects, pipelines, and semantic models are generated automatically. Key Differences Between Platforms Orchestration: SSIS vs Data Factory vs Fabric pipelines Execution environment: on-premise vs cloud vs unified platform Storage model: database vs lakehouse vs OneLake Integration with semantic layers Key Takeaway AnalyticsCreator generates platform-specific warehouse, pipeline, and analytical artifacts from metadata, while execution and runtime behavior are handled by the selected platform.

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