Overview

The Quickstart Deployment Pack (QS DP) is a fixed, integrated bundle of deployment packs and modules that gives you a complete, end-to-end CDF environment in a single deployment: source ingestion, a Quickstart enterprise data model, entity matching, file annotation, contextualization quality monitoring, 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.

Who is this for & when to use

Target personas: Delivery Engineer, CDF Administrator, Solution Architect setting up a new CDF project or evaluating CDF capabilities end-to-end.

Use this pack when:

• You are starting a new CDF project and want a complete, integrated pipeline — ingestion, data model, contextualization, and monitoring — deployed in one step.
• You want to evaluate or demonstrate CDF's full stack (entity matching, P&ID annotation, search, quality reporting) using included synthetic data without live integrations.
• You need a standardized Quickstart 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 (e.g. entity matching or P&ID annotation) — use the individual deployment packs instead to avoid deploying unused modules.
• If your project already has existing modules deployed and you cannot run cdf modules init . --clean — use cdf modules add to select individual constituent modules instead.

Why you need it

Standing up a complete CDF environment from scratch — ingestion pipelines, an enterprise data model, entity matching, P&ID annotation, and quality monitoring — requires assembling, configuring, and sequencing more than a dozen interdependent modules. Getting the dependency order, configuration overrides, and data upload steps right across all of them typically takes days or weeks.

QS DP eliminates that effort by providing a pre-wired, tested bundle that:

• Deploys an entire CDF pipeline in one pass — from source ingestion through contextualization and monitoring, with a single cdf build and cdf deploy.
• Includes synthetic test data — SAP assets, PI time series, SharePoint files, and SAP events are bundled so you can validate the full pipeline without live source integrations.
• Enforces correct module dependency order — the cdf_ingestion workflow stages data population before contextualization, preventing sequencing failures.
• Ships with quality monitoring built in — rpt_quality computes contextualization KPIs from day one, giving you a baseline to track improvement.

What's included

Foundation modules

Source system modules + synthetic data

Contextualization modules

Monitoring and tooling modules

Prerequisites

Verify all of the following before starting deployment:

• Cognite Toolkit version 0.7.33 or later is installed. Follow the setup instructions.
• A cdf.toml file exists in your project root. If missing, run cdf init and select Create toml file (required).
• Authentication is configured and verified — run cdf auth init and cdf auth verify. See the Toolkit authentication docs.
• The data plugin is enabled in cdf.toml — required for the cdf data upload steps:

  [plugins]
  data = true

• The following values are available for your .env file:
  • GROUP_SOURCE_ID — the Object ID of your IDP group. See the IDP Group Setup documentation.
  • OPEN_ID_CLIENT_SECRET — generated from the Open Industrial Data Hub page by clicking Create client secret.

Deployment & installation

Choose your setup path

1. Existing Toolkit project

Use this path to add QS DP modules to an existing Toolkit project without overwriting existing module folders.

a. Point cdf.toml at the Cognite Library

[library.cognite]
url = "https://github.com/cognitedata/library/releases/download/latest/packages.zip"

b. Enable deployment packs in cdf.toml

[alpha_flags]
deployment-pack = true

c. Add the Quickstart modules

From your project directory, run cdf modules add and select Quickstart Deployment Pack from the interactive module list.

2. Starting from scratch (recommended)

Use this path for a clean project dedicated to QS DP. This is the recommended approach.

a. Point cdf.toml at the Cognite Library

[library.cognite]
url = "https://github.com/cognitedata/library/releases/download/latest/packages.zip"

b. Enable deployment packs in cdf.toml

[alpha_flags]
deployment-pack = true

c. Initialize and select the Quickstart modules

In a clean project directory:

cdf modules init . --clean

From the interactive module list, use Space to select Quickstart Deployment Pack, then press Enter to confirm.

d. Verify folder structure

modules/
├── accelerators/
│   ├── cdf_common/
│   ├── cdf_ingestion/
│   ├── contextualization/
│   │   ├── cdf_connection_sql/
│   │   ├── cdf_entity_matching/
│   │   └── cdf_file_annotation/
│   ├── industrial_tools/cdf_search/
│   └── open_industrial_data_sync/
├── sourcesystem/
│   ├── cdf_pi/
│   ├── cdf_sap_assets/
│   ├── cdf_sap_events/
│   └── cdf_sharepoint/
├── dashboards/rpt_quality/
└── models/qs_enterprise_dm/

e. Update the configuration

Open config.dev.yaml (or config.<env>.yaml) and update:

• Set your CDF project name.
• Add GROUP_SOURCE_ID and OPEN_ID_CLIENT_SECRET to your .env file — do not hardcode these in config files.
• Under cdf_entity_matching, update the view configuration:
  • targetViewFilterValues: root:WMT → root:ast_VAL
  • targetViewSearchProperty: name → aliases
  • AssetViewExternalId: YourOrgAsset → Asset
  • TimeSeriesViewExternalId: YourOrgTimeSeries → Enterprise_TimeSeries
  • targetViewExternalId: YourOrgAsset → Asset
  • entityViewExternalId: YourOrgTimeSeries → Enterprise_TimeSeries
• Under cdf_file_annotation, set ApplicationOwner to the owner email address(es).
• Review all cron expressions — placeholder values may be set to February 29 and must be updated to valid dates.

f. Enable FILE_ANNOTATION mode in the SAP asset transformation

Open modules/sourcesystem/cdf_sap_assets/transformations/population/asset.Transformation.sql and:

• Comment out the COMMON MODE block.
• Uncomment the FILE_ANNOTATION MODE block.

This mode sets external IDs as ast_<id>, creates the root node ast_VAL, and populates aliases and tags required for diagram detection during file annotation.

g. Build and deploy

cdf build
cdf deploy --dry-run
cdf deploy

h. Upload synthetic data

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

cdf data upload dir modules/sourcesystem/cdf_pi/upload_data
cdf data upload dir modules/sourcesystem/cdf_sap_assets/upload_data
cdf data upload dir modules/sourcesystem/cdf_sap_events/upload_data
cdf data upload dir modules/sourcesystem/cdf_sharepoint/upload_data
cdf data upload dir modules/accelerators/contextualization/cdf_entity_matching/upload_data
cdf data upload dir modules/accelerators/contextualization/cdf_file_annotation/upload_data

Verify uploads in Integrate → Staging in CDF before triggering workflows.

i. Trigger workflows in order

In the CDF Data Workflows UI, trigger the following workflows in sequence — wait for each to complete before starting the next:

1. ingestion — populates the data model and creates baseline relationships.
2. wf_file_annotation — annotates uploaded P&ID files and creates links to assets.
3. EntityMatching — executes metadata enrichment and entity matching.

After either path, the complete QS DP pipeline is deployed and running in your CDF environment.

Verification

After deployment and workflow execution, perform the following checks:

1. Verify that cdf deploy completed without errors and all modules were deployed successfully.

2. Verify that all cdf data upload commands completed without errors. Navigate to Integrate → Staging and confirm the RAW databases and tables from each module's upload_data/ are present.

3. In Integrate → Data Workflows, confirm that all three workflows (ingestion, wf_file_annotation, EntityMatching) completed without errors.

4. Open Industrial Tools → Search App → Files and confirm that uploaded P&IDs have linked assets — annotations are applied correctly.

5. Review function run logs for dm:context:timeseries:entity_matching in Build Solutions → Functions and confirm the entity matching step processed time series without errors.

6. Navigate to Data Modeling → Data Models and confirm the Quickstart enterprise data model is deployed with its spaces, containers, and views.

7. Run the wf_contextualization_rate workflow, then check tbl_contextualization_rate_report in the db_quality_reports RAW database to confirm the quality monitoring pipeline is producing KPI output.

Configuration reference

All parameters are set in config.dev.yaml (or config.<env>.yaml) at the project root. Sensitive values (GROUP_SOURCE_ID, client IDs and secrets) must be set in your .env file, not hardcoded in config files.

Architecture

QS DP is structured as a fixed bundle of CDF Toolkit modules across four layers. The cdf_ingestion workflow enforces execution order — data population runs before contextualization — so modules at each layer have the data they need when they run.

modules/
├── accelerators/
│   ├── cdf_common/              ← shared spaces, datasets, RAW
│   ├── cdf_ingestion/           ← staged orchestration workflow
│   ├── contextualization/
│   │   ├── cdf_connection_sql/  ← deterministic SQL linking
│   │   ├── cdf_entity_matching/ ← rule-based + ML matching
│   │   └── cdf_file_annotation/ ← P&ID annotation pipeline
│   ├── industrial_tools/
│   │   └── cdf_search/          ← search + location structures
│   └── open_industrial_data_sync/
├── sourcesystem/
│   ├── cdf_pi/                  ← PI time series + synthetic data
│   ├── cdf_sap_assets/          ← SAP assets + synthetic data
│   ├── cdf_sap_events/          ← SAP events + synthetic data
│   └── cdf_sharepoint/          ← files + synthetic P&IDs
├── dashboards/
│   └── rpt_quality/             ← contextualization KPI monitoring
└── models/
    └── qs_enterprise_dm/        ← Quickstart enterprise data model

Workflow execution order

The cdf_ingestion workflow stages execution so that population and contextualization are always sequenced correctly:

1. ingestion — runs source system transformations to populate the data model with assets, time series, events, and files from the synthetic RAW data.
2. wf_file_annotation — annotates P&ID files uploaded by cdf_sharepoint with asset and file links. Runs four internal steps: prepare → launch → finalize → promote.
3. EntityMatching — enriches metadata and then runs rule-based and ML-assisted matching between time series and assets.

FILE_ANNOTATION mode

For P&ID annotation to work, the SAP asset transformation must use FILE_ANNOTATION mode rather than COMMON mode. This mode sets asset external IDs as ast_<id>, creates the root node ast_VAL, and populates aliases and tags that the file annotation pipeline uses for diagram detection. This is a manual SQL edit in asset.Transformation.sql before deployment.

Troubleshooting

For issues not covered here, contact your Cognite support team or post in #topic-deployment-packs on Slack.

Support & ownership