dlt on Dagster

Clarify how the user wants to run dlt (asset type and compute); then follow the Core Workflow or refer to dagster-integrations for external compute.

Quick start: 1) Ask: dlt assets (recommended) vs standard Dagster asset? Run on Dagster compute vs external (ECS, Fargate, etc.)? 2) Follow the Core Workflow or refer to dagster-integrations. 3) Use references as needed.

Integration Approach Decision Tree

START: User wants to build/run a dlt pipeline with Dagster
│
├─→ Run on external compute (ECS, Fargate, or other engine)?
│   Schedule in Dagster, execution elsewhere.
│   │
│   YES → Use the dagster-integrations skill (see "Related skill: dagster-integrations" below).
│         Do not use dlt_assets / Component from this skill for execution.
│   │
│   NO → Run on Dagster compute. Continue:
│   │
├─→ dlt assets (recommended) or standard Dagster asset?
│   │
│   dlt assets (recommended) → Each dlt resource = Dagster asset; selective runs, parallelization.
│   │   Then: Component (defs.yaml + loads.py) or Pythonic (@dlt_assets in code)?
│   │
│   standard Dagster asset → Single asset wrapping the whole dlt pipeline; run-all-or-nothing.
│   │   Use a single @asset that runs dlt.pipeline.run(...). No dlt_assets / Component.

Ask if unclear: When someone wants to build a dlt pipeline on Dagster, ask whether they want dlt assets (recommended) or a standard Dagster asset, and whether they will run on Dagster compute or external compute (ECS, Fargate, etc.).

Component (recommended for dlt assets): Declarative defs in YAML, scaffold-generated structure. Pythonic: All definitions in Python (@dlt_assets, DagsterDltResource). Why dlt assets: Selective runs and parallelization; a single native asset is run-all-or-nothing.

Core Workflow

1. Understand Requirements

• Asset type: dlt assets (recommended) or standard Dagster asset? Ask if not specified.
• Compute: Run on Dagster’s default compute, or schedule in Dagster but run on external compute (ECS, Fargate, etc.)? If external, use the dagster-integrations skill (see below).
• Dagster project: New or existing? Components-ready (defs in YAML) or code-only?
• dlt source: Verified source (e.g. GitHub, Snowflake), declarative REST, or custom Python source?
• Destination: DuckDB, BigQuery, Snowflake, etc.
• Orchestration: One-off materialization, or job/schedule?

2. Prepare Dagster Project

Component approach: Use a components-ready project (or migrate). Create project:

uvx create-dagster project my-project && cd my-project/src
source ../.venv/bin/activate
uv add dagster-dlt

Pythonic approach: Existing Dagster project; add:

uv add dagster-dlt
# or: pip install dagster-dlt

3. Define dlt Loads

Component: Scaffold a dlt component definition (optional --source and --destination pull in dlt source):

dg scaffold defs dagster_dlt.DltLoadCollectionComponent my_ingest --source github --destination snowflake

Then edit defs/<name>/loads.py with your dlt source and pipeline, and defs/<name>/defs.yaml to reference them. See references/component.md.

Pythonic: In Python, define a dlt source (verified, REST, or custom) and a dlt.pipeline. Create a @dlt_assets definition and add DagsterDltResource to Definitions. See references/pythonic.md.

4. Configure Secrets and Config

dlt credentials can be provided via environment variables (recommended with Dagster). Use Dagster’s env/secrets for both Dagster and dlt. No need for a local .dlt/secrets.toml if everything is in env.

Example env vars (names depend on source/destination):

SOURCES__GITHUB__ACCESS_TOKEN=...
DESTINATION__SNOWFLAKE__CREDENTIALS__DATABASE=...
DESTINATION__SNOWFLAKE__CREDENTIALS__PASSWORD=...
# etc.

See references/secrets-and-env.md.

5. Run and Materialize

• Component: dg dev (or your deployment), then in the UI: Assets → select dlt assets → Materialize.
• Pythonic: Same: run Dagster, then materialize the dlt assets from the UI (or via job).

6. Add Jobs and Schedules (Optional)

Define asset jobs that select your dlt assets; attach schedules if you want recurring runs. See references/jobs-and-schedules.md.

7. Cloud deployment (Optional)

Deploy to Dagster Cloud (serverless): connect repo → set env vars in Cloud UI → push; GitHub Actions updates the code location. See references/cloud-deployment.md.

Patterns

Component — loads.py + defs.yaml

In loads.py: define source and pipeline (e.g. source = github_stargazers(...), pipeline = dlt.pipeline(...)). In defs.yaml: type: dagster_dlt.DltLoadCollectionComponent, attributes.loads with source: and pipeline: pointing at those objects. See references/component.md.

Pythonic — @dlt_assets + Definitions

@dlt_assets(dlt_source=..., dlt_pipeline=..., name=..., group_name=...); function takes context and dlt_resource: DagsterDltResource, body yield from dlt_resource.run(context=context). Add DagsterDltResource() to Definitions(resources={"dlt": ...}). See references/pythonic.md.

Component — Customize asset metadata

Use translation in defs.yaml for group_name, description, or metadata (e.g. {{ resource.name }}). See references/component.md §4.

Dynamic incremental/backfill

source.<resource>.apply_hints(incremental=dlt.sources.incremental(..., initial_value=os.getenv("MY_INITIAL_VALUE", "..."))). Optional backfill: only apply when both initial and end env vars set. See references/incremental-backfill-parallel.md.

Parallelization

One asset per resource: source.with_resources(resource_name), unique pipeline name per resource, define_asset_job(..., executor_def=multi_process_executor, config={"max_concurrent": N}). See references/incremental-backfill-parallel.md.

Standard Dagster asset (run-all-or-nothing)

Single @asset that builds dlt.pipeline(...), gets the dlt source, and calls pipeline.run(source). Add to Definitions(assets=[...]). No dagster-dlt or Component; use when the user explicitly wants one asset for the whole pipeline.

Best Practices

• Secrets: Use env vars (Dagster deployment) for dlt; avoid committing .dlt/secrets.toml.
• Component vs Pythonic: Prefer Component for new projects; Pythonic when you already use code-only Definitions.
• dlt sources: Keep resources in a dlt source. For building or debugging dlt pipeline code, use the dlt-skill if available (see “Related skill: dlt-skill” below); this skill is for running pipelines in Dagster.
• Incremental/backfill: Env vars + apply_hints; parallel: one asset per resource, unique pipeline names, multi_process_executor + max_concurrent.

Reference Documentation — When to Read What

• Component setup (scaffold, loads.py, defs.yaml) → references/component.md
• Pythonic setup (@dlt_assets, DagsterDltResource, Definitions) → references/pythonic.md
• Secrets and environment variables for dlt in Dagster → references/secrets-and-env.md
• Jobs and schedules for dlt assets → references/jobs-and-schedules.md
• Incremental loading, backfilling, parallelization → references/incremental-backfill-parallel.md
• Dagster Cloud (serverless) deployment → references/cloud-deployment.md
• Errors, venv, imports, defs paths → references/troubleshooting.md

Further reading

• Deploy dlt with Dagster — dlt docs
• Dagster & dlt (Component) — Component setup
• Dagster & dlt (Pythonic) — @dlt_assets setup

Related skill: dlt-skill

When the task requires building or designing dlt pipelines (choosing verified sources, declarative REST API, custom Python sources, destinations, incremental loading in dlt, or editing .dlt config and secrets), use the dlt-skill if it is available. This skill (dlt-dagster) focuses on running existing dlt pipelines in Dagster; the dlt-skill covers creating and maintaining the dlt pipeline itself.

If the dlt-skill is not installed: Tell the user that a dedicated skill named dlt-skill exists for creating and maintaining dlt pipelines (verified sources, REST API, custom sources, destinations, incremental loading). With the user’s approval, they can install it with:

npx skills add untitled-data-company/data-skills --skill dlt-skill

(Use the repo URL that hosts the dlt-skill if different.)

Related skill: dagster-integrations

When the user wants to schedule a dlt pipeline (or any job) via Dagster but run it on external compute (e.g. ECS, Fargate, or other compute engines), use the dagster-integrations skill if it is available. This skill (dlt-dagster) focuses on running dlt pipelines as Dagster assets on Dagster’s compute; dagster-integrations covers running work on other engines.

If dagster-integrations is not installed: Tell the user that a skill named dagster-integrations exists for running Dagster jobs on external compute (ECS, Fargate, etc.). With the user’s approval, they can install it with:

npx skills add https://github.com/dagster-io/skills --skill dagster-integrations

Templates and Scripts

Asset templates

• assets/templates/loads.py — Minimal Component loads.py (source + pipeline)
• assets/templates/defs.yaml — Minimal Component defs.yaml for one load

No scripts are required; use dg scaffold defs and dg dev / Dagster deployment.

Key Reminders

• Ask when unclear: dlt assets (recommended) vs standard Dagster asset? Run on Dagster compute vs external (ECS, Fargate)? If external compute → use dagster-integrations skill.
• Component: dg scaffold defs dagster_dlt.DltLoadCollectionComponent <name> [--source ...] [--destination ...] → edit loads.py and defs.yaml.
• Pythonic: DagsterDltResource in Definitions; in @dlt_assets call yield from dlt_resource.run(context=context).