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Project

The Project feature provides folder-based repository management for AnalyticsCreator, enabling developers to export and import entire data warehouse configurations as structured file systems. Unlike single-file backups, this approach generates human-readable folder hierarchies compatible with version control systems like Git, facilitating team collaboration, environment migration, and change tracking across development lifecycles.

Project Operations

Core commands for persisting and restoring repository metadata. These operations export individual object definitions into separate files and folders, allowing granular tracking of changes at the component level.

Save Project

Exports the complete repository state to a designated folder structure. Each metadata object type is serialized into dedicated subdirectories as JSON or XML definitions.

  • Export Scope: Captures all connectors, sources, tables, transformations, packages, stars, and deployment configurations.
  • Folder Generation: Creates standardized subdirectories including Connectors, Sources, Tables, Transformations, Packages, Stars, and deployment artifacts.
  • Version Control Ready: Generated folders can be immediately initialized as Git repositories for branch-based development workflows.
  • Metadata Only: Exports object definitions and logic; excludes actual data rows from source systems.

Load Project

Imports a previously exported folder structure into the current repository session. Used to restore configurations or migrate models between environments.

  • Validation: Verifies folder structure integrity and object dependencies before import.
  • Merge Strategy: Imports objects into the current repository; existing objects are updated while new objects are created.
  • Environment Migration: Supports promotion of configurations from Development to Testing to Production environments.
  • Rollback Support: Enables restoration of previous model states by loading tagged repository versions.

Folder Structure

Saved projects generate a standardized directory hierarchy containing 18 specialized folders. Each folder stores specific object types as individual files, enabling granular version control and diff comparisons.

Core Metadata Folders

  • Connectors: Database connection strings and source system configurations.
  • Sources: Source table metadata and extraction definitions.
  • Tables: Data warehouse table schemas including dimensions, facts, and staging tables.
  • Transformations: Column-level transformation logic, calculated fields, and mapping rules.
  • Schemas: Logical database schema definitions and containment rules.

Processing & Deployment Folders

  • Packages: ETL workflow definitions and execution packages.
  • Packages_Historization: Slowly Changing Dimension (SCD) and historization logic.
  • Packages_Import: Source-to-staging import process definitions.
  • Deployments: Environment-specific deployment configurations and target mappings.
  • Layers: Data warehouse layer definitions (Staging, ODS, EDW, Semantic).

Supporting Assets

  • Stars: Data mart star schema configurations and measure definitions.
  • TableReferences: Reusable table reference objects and aliases.
  • SourceReferences: Column mappings and source-to-target references.
  • Macros: Reusable variables and macro expressions.
  • ParamValues: Runtime parameter values and configuration constants.
  • ObjectScripts: Custom SQL scripts, stored procedures, and view definitions.
  • Snapshots: Data snapshot and audit configuration settings.

Use Cases

Typical scenarios where Project-based repository management provides advantages over traditional single-file backups.

  • Version Control Integration: Track changes to individual tables or transformations using Git commit history and branching strategies.
  • Code Review Workflows: Submit pull requests for specific metadata changes rather than entire repository exports.
  • Environment Promotion: Migrate configurations from Development → Test → Production with environment-specific parameter substitution.
  • Disaster Recovery: Maintain point-in-time snapshots of repository states for rapid restoration.
  • Template Distribution: Share standardized data warehouse templates with partners or across organizational units.
  • CI/CD Pipelines: Automate deployment testing using folder-based exports in Azure DevOps or GitHub Actions workflows.