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. Use the search above or jump into a category below.

Pro Tip:

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

Quick Start

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Getting Started

Welcome to the AnalyticsCreator Documentation. In this Getting Started section, you can choose from the following sections: Installation System Requirements Download and Installation Understanding AnalyticsCreator

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.

Reference

AnalyticsCreator Reference guide The AnalyticsCreator is the central storage location for all metadata related to your data warehouse projects. It serves as the foundation for organizing and managing the various elements of a data warehouse, ensuring consistency, scalability, and efficient collaboration across teams. What is the AnalyticsCreator Repository? The repository stores all Data Warehouse Project metadata information, including details about data sources, transformations, layers, and configurations. It is designed to act as a centralized structure where users can: Define and manage data warehouse artifacts. Configure and store database objects and workflows. Organize elements into logical folders for better accessibility. While the repository encompasses all metadata, not every item within it needs to be actively used, allowing flexibility in managing large and complex projects. Repository Structure The repository is organized into folders, with each folder representing a specific Data Warehouse artifact or database object. These objects include but are not limited to: Connectors: Configurations for connecting to external data sources like MSSQL, Oracle, or SAP. Layers: Hierarchical structures for organizing data, such as staging, core, and data marts. Packages: Collections of related objects or configurations for deployment. Indexes: Structures to improve query performance by optimizing data retrieval. Roles: Access controls and permissions for users interacting with the data warehouse. Galaxies, Hierarchies, Partitions, and Parameters: Components used in data modeling to define relationships, subsets, and configurations. Macros and Scripts: Reusable logic and code snippets for data transformations and operations. Object Scripts: Scripts tied to specific data objects for precise customizations. Filters: Tools for selecting or excluding specific data based on defined conditions. Predefined Transformations: Built-in processes to streamline common data processing tasks. Snapshots: Static copies or versions of data at specific points in time for auditing or rollback purposes. Deployments: Configurations and workflows for deploying changes to the data warehouse. Groups: Logical groupings of related objects or users for better management. Models: Representations of the structure and relationships within the data warehouse. Types of Repositories AnalyticsCreator supports three types of repositories, offering flexibility in storage and collaboration: SQL Server Repository Stored in Microsoft SQL Server databases. Ideal for centralized storage and multi-user collaboration in larger projects. Local File Repository Stored locally on your system. Suitable for individual users or small-scale projects requiring minimal setup. AnalyticsCreator Cloud Repository A cloud-based storage solution. Enables seamless collaboration and remote access, making it ideal for distributed teams. Both the SQL Server Repository and Cloud Repository are essentially Microsoft SQL Server databases with a predefined schema to store all AnalyticsCreator metadata. No additional software is required for setup. Key Benefits of the Repository Centralized Management All metadata is stored in one location, ensuring consistency and reducing redundancy. Scalability Supports projects of all sizes, from small, local setups to large, multi-user cloud environments. Flexibility Allows users to organize, customize, and manage artifacts based on project requirements. Collaboration With SQL Server or Cloud repositories, teams can work collaboratively on shared projects. Best Practices for Using the Repository Organize Folders: Group objects logically to reflect the structure and purpose of your data warehouse. Use Appropriate Types: Select the repository type that best suits your project scale and team collaboration needs. Regular Backups: For SQL Server and local repositories, ensure regular backups to prevent data loss. Optimize Performance: Use indexes, filters, and partitions effectively to manage large datasets efficiently. Version Control: Keep track of changes and maintain versioning to facilitate rollback if necessary. The AnalyticsCreator Repository is a robust and versatile solution for managing metadata, enabling you to build scalable and efficient data warehouses. Its flexibility across storage types and comprehensive feature set make it a cornerstone of AnalyticsCreator's functionality. Let me know if you'd like further enhancements! [[doc:19365640354|Read more]]

Manage Objects

In AnalyticsCreator, all objects — such as schemas, tables, attributes, keys, views, and scripts — are centrally stored and maintained in a metadata repository. The platform provides a consistent interface for managing these objects across all project layers, ensuring governance, reuse, and automation readiness. This section introduces the core concepts behind object management in the tool, divided into two categories: common operations and specific operations. Common Operations Common operations represent the actions that are available across most object types in the repository. These include general tasks such as creating, modifying, organizing, or validating metadata entries. The user interface offers consistent patterns for these tasks, enabling fast and structured model development. These operations help ensure that metadata is complete, accurate, and aligned with project standards. They form the foundation of how users interact with objects in any stage of the modeling and deployment lifecycle. Specific Operations Specific operations are contextual and depend on the type of object being edited or the layer in which it exists. These include configurations that control technical behavior, such as relationships, transformations, or deployment properties. They support advanced use cases and deeper control of how the object behaves in staging, EDW, or semantic layers. By managing these object-specific settings, users can apply business rules, enforce data modeling standards, and prepare automation logic that adapts to the requirements of Microsoft Fabric and other target environments.

Tutorials

To become familiar with AnalyticsCreator, we have made certain data sets available. You may use these to test AnalyticsCreator: Click here for the Northwind Data Warehouse

Functions

Get started by clicking on one of these sections: Main Functionality GUI Process support Data Sources Export Functionality Use of Analytics Frontends

Unnamed Category

EXECUTIVE SUMMARY Reference Guide Structure Analysis Structural overview and HubDB 3-level mapping feasibility 4 Top-Level Sections 44 Subsections 189 Topic Pages 3 Hierarchy Levels Structure Overview The Reference Guide is organized into a clean 3-level hierarchy. The spreadsheet uses columns Menu → Submenu → Subsubmenu to define the tree. Each entry also carries an ID, description, AC visual element reference, and multiple "call from" paths (navigation tree, toolbar, diagram, visual element). SECTION SUBSECTIONS (L2) TOPICS (L3) MAX DEPTH 1. User interface 8 127 3 levels 2. Entity types 9 62 3 levels 3. Entities 17 0 2 levels 4. Parameters 10 0 2 levels Sections 1 and 2 use the full 3-level depth. Sections 3 and 4 are 2-level only (Menu → Submenu with no sub-items). 1. User interface The largest section (127+ topics) covering all visual aspects of the application. Contains 8 subsections: Common Information, Toolbar (9 items), Navigation Tree (18 items), Dataflow Diagram (14 items), Pages (26 items), Lists (30 items), Dialogs (17 items), and Wizards (13 items). 2. Entity types Documents all type classifications across 9 subsections: Connector Types (12), Source Types (5), Table Types (9, including DataVault), Transformation Types (7), Transformation Historization Types (5), Join Historization Types (5), Package Types (7), SQL Script Types (7), and Schema Types (5). Total: 62 topics. 3. Entities Covers 17 core entity definitions: Layer, Schema, Connector, Source, Table, Transformation, Package, Index, Partition, Hierarchy, Macro, SQL Script, Object Script, Deployment, Object Group, Filter, and Model. Flat structure with no further nesting. 4. Parameters Documents 10 configuration parameters (AC_LOG, TABLE_COMPRESSION_TYPE, PERS_DEFAULT_PARTSWITCH, DIAGRAM_NAME_PATTERN, and more), plus an "Other Parameters" catch-all page. Two-level structure only. HubDB 3-Level Mapping Can this structure fit into a HubDB table with three columns: Category → Section → Topic? ✓ Yes — this is a natural fit. The spreadsheet's Menu → Submenu → Subsubmenu hierarchy maps directly to a 3-level HubDB schema. The two shallow sections (Entities and Parameters) simply leave the Topic column null or use the item as both Section and Topic. HUBDB COLUMN MAPS TO COUNT EXAMPLES Level 1: Category Menu column 4 User Interface, Entity Types, Entities, Parameters Level 2: Section Submenu column 44 Toolbar, Navigation Tree, Connector Types, Pages, Lists Level 3: Topic Subsubmenu column 189 File, MSSQL, Import, Historization, DWH Wizard, Login Sample HubDB Rows ID CATEGORY SECTION TOPIC 1.2.2 User Interface Toolbar File 1.2.6 User Interface Toolbar ETL 1.3.2 User Interface Navigation Tree Connectors 1.3.3 User Interface Navigation Tree Layers 1.3.4 User Interface Navigation Tree Packages 1.3.5 User Interface Navigation Tree Indexes 1.3.6 User Interface Navigation Tree Roles 1.3.7 User Interface Navigation Tree Galaxies 1.3.8 User Interface Navigation Tree Hierarchies 1.3.9 User Interface Navigation Tree Partitions 2.1.1 Entity Types Connector Types MSSQL 3.5 Entities Table null 4.1 Parameters AC_LOG null Considerations ✓ Clean 3-Level Fit Menu → Submenu → Subsubmenu maps 1:1 to Category → Section → Topic with no restructuring needed. ✓ Consistent IDs Every row has a hierarchical ID (e.g., 1.5.12) usable as a unique slug or sort key in HubDB. ✓ Metadata-Ready Extra columns (Description, AC Element, Call Paths) store as additional HubDB columns alongside the 3-level hierarchy. ⚠ Shallow Sections Sections 3 (Entities) and 4 (Parameters) are only 2 levels deep. The Topic column will be null for ~27 rows. Use a default or mirror the Section name. AnalyticsCreator Reference Guide — Structure Analysis • Generated from ReferenceGuideStructure.xlsx