GoodData Export

A Python library for exporting GoodData workspace metadata to SQLite databases and CSV files.

Features

Multiple Export Formats: Export to SQLite, CSV, or both
Multi-Workspace Support: Process parent and child workspaces in parallel
Local Layout JSON Support: Process local JSON-based layout files without API calls
Flexible Configuration: Configure via Python API or environment variables
Post-Processing: Automatic duplicate detection and relationship analysis
Rich Text Extraction: Optional extraction of metrics/insights from dashboard rich text widgets
Standalone: Zero Flask dependencies - pure Python library
Clean CSV Exports: Automatically clears CSV directory before each export to prevent stale data

Installation

From Git

# With uv
uv pip install git+https://github.com/vondravl/gooddata-export.git

# With pip
pip install git+https://github.com/vondravl/gooddata-export.git

From source (local development)

git clone https://github.com/vondravl/gooddata-export.git
cd gooddata-export

# With uv (recommended)
uv sync

# With pip
pip install -e ".[dev]"

Quick Start

Command Line Interface

Create a .env.gdcloud configuration file:

BASE_URL=https://your-instance.gooddata.com
WORKSPACE_ID=your_workspace_id
BEARER_TOKEN=your_api_token

Run the export:

# Basic export (both SQLite and CSV)
gooddata-export export

# Export only SQLite (fastest)
gooddata-export export --format sqlite

# Export with child workspaces
gooddata-export export --include-child-workspaces --max-workers 10

# Custom directories
gooddata-export export --db-dir my_databases --csv-dir my_csvs

# Enable debug mode
gooddata-export export --debug

# Run enrichment on existing database
gooddata-export enrich --db-path output/db/gooddata_export.db

# Get help
gooddata-export --help

Python API

from gooddata_export import export_metadata

result = export_metadata(
    base_url="https://your-instance.gooddata.com",
    workspace_id="your_workspace_id",
    bearer_token="your_api_token"
)

print(f"Database created at: {result['db_path']}")  # output/db/gooddata_export.db
print(f"CSV files in: {result['csv_dir']}")  # output/metadata_csv/
print(f"Processed {result['workspace_count']} workspace(s)")

Using Environment Variables (Python API)

Create a .env.gdcloud file:

BASE_URL=https://your-instance.gooddata.com
WORKSPACE_ID=your_workspace_id
BEARER_TOKEN=your_api_token

Then in Python:

from gooddata_export.config import ExportConfig
from gooddata_export.export import export_all_metadata

# Load config from .env files
config = ExportConfig(load_from_env=True)

result = export_all_metadata(
    config=config,
    output_dir="output"
)

CLI Options

Commands

gooddata-export export - Export metadata from GoodData
gooddata-export enrich - Run post-export enrichment on existing database

Connection Options

--base-url URL - GoodData API base URL (overrides .env.gdcloud)
--workspace-id ID - Workspace ID to export (overrides .env.gdcloud)
--bearer-token TOKEN - API authentication token (overrides .env.gdcloud)

Export Configuration

--db-dir DIR - Directory for SQLite database files (default: output/db)
--csv-dir DIR - Directory for CSV export files (default: output/metadata_csv)
--format {sqlite,csv} - Export format(s): sqlite, csv, or both (default: both)
--db-name FILENAME - Custom SQLite database filename (default: gooddata_export.db)

Child Workspace Options

--include-child-workspaces - Include child workspaces in export
--child-workspace-data-types {metrics,dashboards,visualizations,filter_contexts} - Data types to fetch from children
--max-workers N - Maximum parallel workers (default: 5)

Feature Flags

--enable-rich-text-extraction - Enable extraction from rich text widgets
--skip-post-export - Skip post-export SQL processing (duplicate detection)
--debug - Enable debug logging

Examples

# SQLite only (fastest)
gooddata-export export --format sqlite --skip-post-export

# CSV only
gooddata-export export --format csv

# Multi-workspace with specific data types
gooddata-export export --include-child-workspaces --child-workspace-data-types dashboards visualizations --max-workers 15

# Override config with command-line args
gooddata-export export --workspace-id prod_workspace --db-dir exports/prod/db --debug

Usage Examples

SQLite-Only Export (Fastest)

For maximum speed, export only to SQLite and skip post-processing:

from gooddata_export import export_metadata

result = export_metadata(
    base_url="https://your-instance.gooddata.com",
    workspace_id="your_workspace_id",
    bearer_token="your_token",
    export_formats=["sqlite"],  # SQLite only
    run_post_export=False       # Skip duplicate detection
)

This is ideal for:

Programmatic access to metadata
Custom post-processing pipelines
Integration with other tools

Multi-Workspace Export

Export from a parent workspace and all its children:

result = export_metadata(
    base_url="https://your-instance.gooddata.com",
    workspace_id="parent_workspace_id",
    bearer_token="your_token",
    include_child_workspaces=True,
    child_workspace_data_types=["dashboards", "visualizations"],
    max_parallel_workspaces=5  # Process 5 workspaces at once (default)
)

Local Layout JSON Export (No API Calls)

Process local layout files without connecting to GoodData API. This is useful for:

Tagging workflows on feature branches before changes are deployed
Offline analysis of exported layout files
CI/CD pipelines without API access

import json
from gooddata_export import export_metadata

# Load layout from file (exported via gooddata-cli or API)
with open("layout.json") as f:
    layout = json.load(f)

result = export_metadata(
    base_url="https://your-instance.gooddata.com",  # Used for URL generation only
    workspace_id="my_workspace",
    layout_json=layout,  # No API calls made
    export_formats=["sqlite"],
    run_post_export=True
)

Expected layout format:

{
  "analytics": {
    "metrics": [...],
    "visualizationObjects": [...],
    "analyticalDashboards": [...],
    "filterContexts": [...],
    "dashboardPlugins": [...]
  },
  "ldm": {
    "datasets": [...],
    ...
  }
}

Note: When using layout_json, tables that would be stale (users, user_groups, user_group_members) are automatically truncated.

Complete Export with All Features

result = export_metadata(
    base_url="https://your-instance.gooddata.com",
    workspace_id="your_workspace_id",
    bearer_token="your_token",
    export_formats=["sqlite", "csv"],
    enable_rich_text_extraction=True,
    run_post_export=True,
    debug=True
)

Configuration Options

Required Parameters

base_url: GoodData API base URL
workspace_id: Workspace ID to export
bearer_token: API authentication token (required unless layout_json is provided)

Optional Parameters

layout_json: Local layout data dict - when provided, skips API fetch and uses this data directly
export_formats: List of ["sqlite"], ["csv"], or both (default: both)
include_child_workspaces: Fetch data from child workspaces (default: False)
- Note: The workspaces table is always created with child workspace list; this flag controls whether to fetch child workspace DATA (metrics, dashboards, etc.)
child_workspace_data_types: Data types to fetch from children (default: all)
- Options: "metrics", "dashboards", "visualizations", "filter_contexts"
max_parallel_workspaces: Parallel processing limit (default: 5)
enable_rich_text_extraction: Extract from rich text widgets (default: False)
run_post_export: Run duplicate detection SQL (default: True)
debug: Enable debug logging (default: False)
db_name: Custom database path (default: output_dir/db/gooddata_export.db)

Output Structure

Note: Before each export, the CSV directory (output/metadata_csv/) is automatically cleaned to prevent stale data from mixing with new exports. Database files naturally overwrite themselves and are not cleaned, allowing you to keep workspace-specific databases from multiple exports.

SQLite Database

The SQLite database contains the following tables:

metrics: Metric definitions, MAQL, and metadata
visualizations: Visualization configurations
dashboards: Dashboard definitions and layouts
ldm_datasets: Logical data model datasets with tags
ldm_columns: LDM columns (attributes, facts, references) with tags
ldm_labels: Attribute label definitions (display forms)
filter_contexts: Filter context definitions
filter_context_fields: Individual filters within each filter context (date filters and attribute filters)
workspaces: Workspace information (always included; child workspaces listed when available)
visualizations_references: Visualization references to metrics, facts, and labels
dashboards_visualizations: Visualization-to-dashboard relationships
dashboards_metrics: Metric-to-dashboard relationships (rich text only)
dashboards_references: Dashboard-level references to labels, datasets, and filter contexts
dictionary_metadata: Export metadata (timestamp, workspace ID, etc.)
metrics_references: All metric references extracted from MAQL - metrics, attributes, labels, and facts (created by post-export)
metrics_ancestry: Full transitive metric ancestry (created by post-export)

CSV Files

When CSV export is enabled, the following files are created:

gooddata_metrics.csv
gooddata_visualizations.csv
gooddata_dashboards.csv
gooddata_ldm_datasets.csv
gooddata_ldm_columns.csv
gooddata_ldm_labels.csv
gooddata_filter_contexts.csv
gooddata_filter_context_fields.csv
gooddata_workspaces.csv (always included; child workspaces listed when available)
gooddata_visualizations_references.csv
gooddata_dashboards_visualizations.csv
gooddata_dashboards_metrics.csv (rich text only)

Post-Export Processing

When run_post_export=True (default for single workspace exports), the library runs SQL scripts to:

Build metric relationships: Extracts metric-to-metric references from MAQL formulas
Compute metric ancestry: Creates transitive closure of metric dependencies
Detect duplicates: Identifies visualizations and metrics with identical content
Track usage: Marks which metrics/visualizations are used in dashboards
Create analytical views: Tag views, usage views, relationship views

Key views created:

v_metrics_relationships_* - Metric dependency analysis and tag inheritance
v_metrics_usage, v_visualizations_usage - Usage tracking
v_*_tags - Unnested tag views for filtering

See USAGE_GUIDE.md for detailed post-processing documentation.

Note: Post-export processing is automatically skipped for multi-workspace exports to avoid confusion.

Performance Tuning

For Large Multi-Workspace Deployments (1000+ workspaces)

result = export_metadata(
    base_url="...",
    workspace_id="...",
    bearer_token="...",
    include_child_workspaces=True,
    child_workspace_data_types=["dashboards"],  # Fetch only dashboards
    max_parallel_workspaces=20,  # Higher parallelization
    export_formats=["sqlite"],   # Skip CSV
    run_post_export=False        # Skip post-processing
)

Expected performance: 10-20 workspaces/minute

For Smaller Deployments (<100 workspaces)

result = export_metadata(
    base_url="...",
    workspace_id="...",
    bearer_token="...",
    include_child_workspaces=True,
    child_workspace_data_types=["metrics", "dashboards", "visualizations", "filter_contexts"],
    max_parallel_workspaces=8
)

Development

Running Tests

# With uv
uv sync
uv run pytest

# With pip
pip install -e ".[dev]"
pytest

Project Structure

gooddata-export/
├── gooddata_export/           # Core library package
│   ├── __init__.py           # Main API exports
│   ├── cli/                  # Command-line interface
│   │   ├── __init__.py       # Package exports (main function)
│   │   ├── main.py           # CLI commands and argument parsing
│   │   └── prompts.py        # Interactive prompt utilities
│   ├── config.py             # Configuration handling
│   ├── constants.py          # Shared constants
│   ├── common.py             # API client utilities
│   ├── db.py                 # Database utilities
│   ├── post_export.py        # Post-processing orchestration
│   ├── export/               # Export orchestration
│   │   ├── __init__.py       # Main orchestration (export_all_metadata)
│   │   ├── fetch.py          # Data fetching functions (API calls)
│   │   ├── writers.py        # Database/CSV writer functions
│   │   └── utils.py          # Export utilities
│   ├── process/              # Data processing logic
│   │   ├── __init__.py       # Exports all process functions
│   │   ├── entities.py       # Entity processing
│   │   ├── layout.py         # Layout API fetching
│   │   ├── dashboard_traversal.py  # Dashboard widget extraction
│   │   ├── rich_text.py      # Rich text extraction
│   │   └── common.py         # Shared utilities
│   └── sql/                  # SQL scripts (auto-executed during post-export)
│       ├── procedures/       # Stored procedures and automation views
│       ├── updates/          # Data enrichment scripts (duplicates, usage analysis)
│       ├── views/            # Analytical views (dependencies, tags, usage)
│       └── *.yaml, *.md      # Execution config and documentation
├── main.py                   # Development CLI wrapper (convenience for local dev)
├── pyproject.toml            # Package configuration
├── README.md                 # This file
├── LICENSE                   # MIT License
├── USAGE_GUIDE.md            # Detailed usage examples
├── .env.gdcloud              # Configuration file (create this)
└── output/                   # Export destination (auto-created)
    ├── db/                   # SQLite databases
    └── metadata_csv/         # CSV exports

Note: The sql/ directory contains various analytical scripts that are automatically applied during post-export processing. These scripts evolve frequently as new analysis capabilities are added.

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please submit pull requests or open issues on GitHub.

Support

For issues and questions, please open an issue on GitHub.

Name		Name	Last commit message	Last commit date
Latest commit History 104 Commits
.github		.github
gooddata_export		gooddata_export
scripts		scripts
tests		tests
.env.gdcloud.example		.env.gdcloud.example
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
USAGE_GUIDE.md		USAGE_GUIDE.md
main.py		main.py
pyproject.toml		pyproject.toml

License

vondravl/gooddata-export

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GoodData Export

Features

Installation

From Git

From source (local development)

Quick Start

Command Line Interface

Python API

Using Environment Variables (Python API)

CLI Options

Commands

Connection Options

Export Configuration

Child Workspace Options

Feature Flags

Examples

Usage Examples

SQLite-Only Export (Fastest)

Multi-Workspace Export

Local Layout JSON Export (No API Calls)

Complete Export with All Features

Configuration Options

Required Parameters

Optional Parameters

Output Structure

SQLite Database

CSV Files

Post-Export Processing

Performance Tuning

For Large Multi-Workspace Deployments (1000+ workspaces)

For Smaller Deployments (<100 workspaces)

Development

Running Tests

Project Structure

License

Contributing

Support

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