This release is a pre-release and may not be stable for production use.
GoodData Pipelines
A high-level library for automating the lifecycle of GoodData Cloud (GDC).
You can use the package to manage following resources in GDC:
- Provisioning (create, update, delete)
- User profiles
- User Groups
- User/Group permissions
- User Data Filters
- Child workspaces (incl. Workspace Data Filter settings)
- Backup and restore of workspaces
- Create and backup snapshots of workspace metadata to local storage, AWS S3, or Azure Blob Storage
- LDM Extension
- extend the Logical Data Model of a child workspace with custom datasets and fields
In case you are not interested in incorporating a library in your own program but would like to use a ready-made script, consider having a look at GoodData Productivity Tools.
Provisioning
The entities can be managed either in full load or incremental way.
Full load means that the input data should represent the full and complete desired state of GDC after the script has finished. For example, you would include specification of all child workspaces you want to exist in GDC in the input data for workspace provisioning. Any workspaces present in GDC and not defined in the source data (i.e., your input) will be deleted.
On the other hand, the incremental load treats the source data as instructions for a specific change, e.g., a creation or a deletion of a specific workspace. You can specify which workspaces you would want to delete or create, while the rest of the workspaces already present in GDC will remain as they are, ignored by the provisioning script.
The provisioning module exposes Provisioner classes reflecting the different entities. The typical usage would involve importing the Provisioner class and the data input data model for the class and planned provisioning method:
import os
import logging
from csv import DictReader
from pathlib import Path
# Import the Entity Provisioner class and corresponding model from the gooddata_pipelines library
from gooddata_pipelines import UserFullLoad, UserProvisioner
# Create the Provisioner instance - you can also create the instance from a GDC yaml profile
provisioner = UserProvisioner(
host=os.environ["GDC_HOSTNAME"], token=os.environ["GDC_AUTH_TOKEN"]
)
# Optional: set up logging and subscribe to logs emitted by the provisioner
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
provisioner.logger.subscribe(logger)
# Load your data from your data source
source_data_path: Path = Path("path/to/some.csv")
source_data_reader = DictReader(source_data_path.read_text().splitlines())
source_data = [row for row in source_data_reader]
# Validate your input data
full_load_data: list[UserFullLoad] = UserFullLoad.from_list_of_dicts(
source_data
)
# Run the provisioning
provisioner.full_load(full_load_data)
Ready-made scripts covering the basic use cases can be found here in the GoodData Productivity Tools repository.
Backup and Restore of Workspaces
The backup and restore module allows you to create snapshots of GoodData Cloud workspaces and restore them later. Backups can be stored locally, in AWS S3, or Azure Blob Storage.
import os
from gooddata_pipelines import BackupManager
from gooddata_pipelines.backup_and_restore.models.storage import (
BackupRestoreConfig,
LocalStorageConfig,
StorageType,
)
# Configure backup storage
config = BackupRestoreConfig(
storage_type=StorageType.LOCAL,
storage=LocalStorageConfig(),
)
# Create the BackupManager instance
backup_manager = BackupManager.create(
config=config,
host=os.environ["GDC_HOSTNAME"],
token=os.environ["GDC_AUTH_TOKEN"]
)
# Backup specific workspaces
backup_manager.backup_workspaces(workspace_ids=["workspace1", "workspace2"])
# Backup workspace hierarchies (workspace + all children)
backup_manager.backup_hierarchies(workspace_ids=["parent_workspace"])
# Backup entire organization
backup_manager.backup_entire_organization()
For S3 or Azure Blob Storage, configure the appropriate storage type and credentials in BackupRestoreConfig.
Bugs & Requests
Please use the GitHub issue tracker to submit bugs or request features.
Changelog
See GitHub releases for released versions and a list of changes.
1 maintainer
Metadata
1 maintainer
Release files for gooddata-pipelines 1.73.1.dev4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gooddata_pipelines-1.73.1.dev4.tar.gz | 101.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gooddata_pipelines-1.73.1.dev4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 202.3 kB
Release files / gooddata_pipelines-1.73.1.dev4.tar.gz
| Download URL | gooddata_pipelines-1.73.1.dev4.tar.gz |
|---|---|
| Size | 101.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d651b12c28b611e1772e58addbb4e953de9087985cc2ff93fdf725bef4226cb5
|
|
BLAKE2b-256 checksum How to use checksums |
e52a07abb785901befe6e560c3a6badc53fc69ba1a6c8790c955f9b055b94103
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 28, 2026.
Transparency logRelease files / gooddata_pipelines-1.73.1.dev4-py3-none-any.whl
| Download URL | gooddata_pipelines-1.73.1.dev4-py3-none-any.whl |
|---|---|
| Size | 101.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
2e21fdf7ae03fa6cba3838585de6505eba22b418d5f4d06fde157adea625a523
|
|
BLAKE2b-256 checksum How to use checksums |
ccfedbdef235f234d03e061daa91dd9df70da9b02ef5c519bd506dff41731693
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 28, 2026.
Transparency log
