Foundation Demo DP reference

The Foundation Demo DP (dp:quickstart) is an integrated bundle of deployment packs and modules that stands up a complete, end-to-end CDF environment in a single deployment: persona-based access groups, synthetic source ingestion, a CFIHOS enterprise data model, entity matching, file annotation, and synthetic test data. It is the fastest way to get a fully functional CDF project running with real workflows — from raw source data through contextualized assets — without assembling individual packs manually.

The Foundation Demo DP is a fixed bundle (canCherryPick = false) — modifying or cherry-picking individual modules within the pack is not supported. In the Toolkit menu it appears as Foundation Deployment Pack Demo. If you need a customized setup, add the constituent modules individually with cdf modules add. For a production baseline rather than a demo, use the Foundation deployment pack.

Who it’s for

Target personas:

Data engineers — deploy and configure the demo bundle end-to-end.
Solution architects — evaluate CDF capabilities before committing to a project-specific model.
Field engineers — showcase CDF capabilities in a matter of minutes using included synthetic data.

Use this pack when:

You are starting a new CDF project and want a complete, integrated pipeline — access groups, ingestion, data model, and contextualization — deployed in one step.
You want to evaluate or demonstrate CDF’s full stack (entity matching, P&ID annotation) using included synthetic data without live integrations.
You need a standardized baseline that your team can then customize module by module.
You want to validate CDF capabilities before committing to a project-specific data model.

When not to use this pack:

If you only need one specific capability (for example, entity matching or P&ID annotation) — use the individual deployment packs instead to avoid deploying unused modules.
If you are building a governed production environment rather than a demo — use the Foundation deployment pack.

What’s included

The bundle deploys 9 modules across four layers. Unlike earlier versions of this pack, the module list no longer includes a standalone search module or a quality-monitoring dashboard, and it now ships the same persona access groups and setup wizard as the Foundation pack.

Foundation and orchestration

Module	Description	Optional?
`cdf_project_foundation`	Three persona-led access groups (consumer, producer, admin) and the interactive project setup wizard — shared with the Foundation pack. See Configuration reference.	No
`cdf_ingestion`	Orchestration workflow that runs staged pipelines — data population first, then contextualization.	No

Data model

Module	Description	Optional?
`cfihos_oil_and_gas_extension`	CFIHOS enterprise data model for oil and gas assets.	No
`cfihos_oil_and_gas_extension_search`	Search-optimized solution views on top of the CFIHOS enterprise model.	No

Source systems and synthetic data

Module	Description	Optional?
`cdf_sap_data_dump`	Ingests and transforms synthetic SAP functional locations, equipment, and maintenance data directly into the CFIHOS model.	No
`cdf_pi_data_dump`	Creates time series entities and metadata from synthetic PI time series data.	No
`cdf_sharepoint_data_dump`	Loads synthetic files for annotation, including the P&IDs used in testing.	No

Contextualization

Module	Description	Optional?
`cdf_entity_matching`	Matches time series and assets using rule-based and ML-assisted methods. See CDF Entity Matching.	No
`cdf_file_annotation`	Runs prepare, launch, finalize, and promote steps for P&ID file annotations.	No

Prerequisites

Verify all of the following before you start:

Cognite Toolkit version 0.8 or later is installed. See Setting up.
A cdf.toml file exists in your project root. If it is missing, run cdf modules init . and select Create toml file (required).
The data plugin is enabled in cdf.toml — required for the cdf data upload steps:
[plugins] data = true
Authentication is configured and verified — run cdf auth init and cdf auth verify. See Authentication and authorization.
The Microsoft Entra ID group object IDs for your consumer, producer, and admin personas are available — the setup wizard prompts for these and stores them in .env as CONSUMER_SOURCE_ID, PRODUCER_SOURCE_ID, and ADMIN_SOURCE_ID.

Install and deploy

Because the Foundation Demo DP is a fixed bundle, deploying into a clean project is recommended.

Start a new project (recommended)
Add to an existing project

In a clean directory, initialize and select the bundle:
cdf modules init . --clean
--clean overwrites existing module folders in the current directory. Use a fresh directory or commit your changes first.
From the interactive module list, use Space to select Foundation Deployment Pack Demo, then press Enter to confirm.

Run cdf modules add and select Foundation Deployment Pack Demo from the interactive list.

The Foundation Demo DP is a fixed bundle and may conflict with modules you have already deployed. Review your existing module structure and commit local changes before proceeding.

Then run the interactive setup wizard from the project root:

python modules/common/cdf_project_foundation/scripts/setup_project.py

The wizard shows its resolved pack and data model variant, then prompts for CDF project names, site or location, the Entra ID group object IDs for each persona, source-system owner contacts, and (because cdf_file_annotation is installed) the Streamlit ApplicationOwner email. It writes all config.<env>.yaml files and .env in one pass, and removes any redundant auth files.

Re-running the wizard is idempotent — it pre-fills current values, so it is safe to run again. For non-interactive use, pass -y to skip the confirmation prompt, or --check to validate an existing configuration without writing changes.

Before building, review all cron expressions in the generated config — placeholder values may be set to February 29 and must be changed to valid recurring dates. Then build and deploy:

Build deployment artifacts

cdf build

The Toolkit substitutes template variables and writes artifacts to the build/ directory. If you need to change the project name or any variables marked <change_me>, edit config.<env>.yaml first — see Configure, build, and deploy modules.

Dry-run the deployment

cdf deploy --dry-run

Inspect the output and confirm that configurations look correct before deploying.

Deploy to CDF

cdf deploy

The Toolkit deploys only configurations that have changed since the last run.

Set up CI/CD (optional)

For governed production deployments, automate build, dry-run, and deploy in a pipeline. See Set up CI/CD pipelines.

A warning about non-resource directories such as upload_data in cdf_pi_data_dump is expected and can be ignored.

After deployment, upload the synthetic test data for the source and contextualization modules:

cdf data upload dir modules/sourcesystem/cdf_pi_data_dump/upload_data
cdf data upload dir modules/sourcesystem/cdf_sap_data_dump/upload_data
cdf data upload dir modules/sourcesystem/cdf_sharepoint_data_dump/upload_data
cdf data upload dir modules/contextualization/cdf_entity_matching/upload_data
cdf data upload dir modules/contextualization/cdf_file_annotation/upload_data

If your modules directory is under an organization directory, prepend that directory name to each path. To skip project-name verification in test environments, add --skip-verify-cdf-project to each upload command.

Finally, in the CDF Data Workflows UI, trigger the workflows in order — wait for each to finish before starting the next:

ingestion — populates the CFIHOS data model and creates baseline relationships.
wf_file_annotation — annotates the uploaded P&ID files and links them to assets.
EntityMatching — runs metadata enrichment and entity matching.

Verify the deployment

After deployment and workflow execution, confirm success with these checks:

The cdf deploy command finishes without errors and all modules deploy successfully.
All cdf data upload commands finish without errors — in Integrate > Staging (RAW), confirm the tables from each module’s upload_data/ are present.
In CDF, the three persona groups (for example, consumer_all_dev, producer_all_dev, and admin_all_dev) are present and bound to the correct Entra ID group object IDs.
In Integrate > Data Workflows, confirm the three workflows (ingestion, wf_file_annotation, EntityMatching) completed without errors.
Open Industrial Tools > Search > Files and confirm that uploaded P&IDs have linked assets — annotations are applied.
In Build Solutions > Functions, review the logs for the entity matching function and confirm time series were processed without errors.
In Data Modeling > Data Models, confirm the CFIHOS oil and gas enterprise data model is deployed with its spaces, containers, and views.

The deployment is successful when all three workflows complete and annotations and matches appear in the data model.

Configuration reference

Most parameters are written by the setup wizard (modules/common/cdf_project_foundation/scripts/setup_project.py) into config.<env>.yaml and .env at the project root. Sensitive values (Entra ID group object IDs, client IDs, and secrets) are stored in .env, not hardcoded.

Parameter	Module	Description
CDF project name	All	Your CDF project external ID for the target environment. Set via the setup wizard.
`consumerSourceId` / `producerSourceId` / `adminSourceId`	`cdf_project_foundation`	Microsoft Entra ID group object IDs per persona, stored in `.env` as `CONSUMER_SOURCE_ID`, `PRODUCER_SOURCE_ID`, `ADMIN_SOURCE_ID`. Set via the setup wizard.
`site`	`cdf_project_foundation`	Optional site/location segment used in access-group and instance-space names. Set via the setup wizard.
`ApplicationOwner`	`cdf_file_annotation`	Email address(es) of the application owner for the file annotation pipeline. Set via the setup wizard (prompted because `cdf_file_annotation` is installed).
Cron expressions	All scheduled modules	Placeholder cron values may be set to February 29 — update to valid recurring dates before deploying. Not set by the wizard; review manually.

See the Foundation configuration reference for the full list of cdf_project_foundation parameters (dataset, schemaSpace, instanceSpace, instanceSpaces, additionalSchemaSpaces, dataModelVariant, and the persona group names) — the Demo pack uses the same module with dataModelVariant: cfihos_oil_and_gas_extension fixed by the bundle.

Architecture

The Foundation Demo DP is a fixed bundle of Cognite Toolkit modules across four layers. The cdf_ingestion workflow enforces execution order — data population runs before contextualization — so each layer has the data it needs when it runs.

modules/
├── common/
│   ├── cdf_project_foundation/  ← persona access groups + setup wizard
│   └── cdf_ingestion/           ← staged orchestration workflow
├── sourcesystem/
│   ├── cdf_pi_data_dump/          ← PI time series + synthetic data
│   ├── cdf_sap_data_dump/         ← SAP assets, equipment, work orders + synthetic data
│   └── cdf_sharepoint_data_dump/  ← files + synthetic P&IDs
├── contextualization/
│   ├── cdf_entity_matching/ ← rule-based + ML matching
│   └── cdf_file_annotation/ ← P&ID annotation pipeline
└── datamodels/
    ├── cfihos_oil_and_gas_extension/         ← CFIHOS enterprise data model
    └── cfihos_oil_and_gas_extension_search/  ← search-optimized solution views

Pipeline execution order

Troubleshooting

Symptom	Likely cause	Resolution
Module files overwritten unexpectedly	Ran `cdf modules init . --clean` in a directory with existing work.	Use a clean directory, or commit existing work first. Use `cdf modules add` to add modules to an existing project without overwriting.
`cdf data upload` fails with “data plugin not enabled”	`[plugins] data = true` is missing from `cdf.toml`.	Add `data = true` under `[plugins]` in `cdf.toml` and retry.
`cdf data upload` fails with a project verification error	The project name in the upload manifest does not match the configured CDF project.	Add `--skip-verify-cdf-project` for test environments, or align the project name in `Manifest.yaml` with your CDF project.
Deploy fails on access groups	`*_SOURCE_ID` values missing or wrong in `.env`.	Re-run the setup wizard (`setup_project.py`) to confirm the Entra ID group object IDs, or run `setup_project.py --check` to validate without writing changes.
Wizard asks you to choose between “foundation” and “demo”	Both `_extractor` and `_data_dump` source-system modules are present in the project.	Keep only the `*_data_dump` modules that ship with this bundle, or answer the prompt to resolve manually.
Workflow fails on the first run after deploy	Synthetic data was not uploaded before triggering workflows, or an upload failed.	Verify all `cdf data upload` commands completed and the expected tables are visible in Integrate > Staging (RAW) before triggering workflows.
Scheduled workflows fail to trigger	Cron expressions are set to an invalid date (for example, February 29 in a non-leap year).	Update all cron expressions in the config to valid recurring dates before deploying.
`cdf deploy` fails with “Insufficient permissions”	Toolkit service account is missing capabilities required by one or more modules.	Add capabilities for Functions, Workflows, Transformations, RAW, Data Sets, Data Modeling, and Extraction Pipelines to the CDF group used by the Toolkit.

For issues not covered here, contact Cognite support.

Support

Field	Details
Team	Cognite Value Delivery Team
Documentation	Cognite product documentation
Questions and feedback	Deployment Packs group on Cognite Hub
Version	1.0.0
Last updated	2026

​Who it’s for

​What’s included

​Foundation and orchestration

​Data model

​Source systems and synthetic data

​Contextualization

​Prerequisites

​Install and deploy

​Verify the deployment

​Configuration reference

​Architecture

​Pipeline execution order

​Troubleshooting

​Support

Who it’s for

What’s included

Foundation and orchestration

Data model

Source systems and synthetic data

Contextualization

Prerequisites

Install and deploy

Verify the deployment

Configuration reference

Architecture

Pipeline execution order

Troubleshooting

Support