dlt Pipeline Creator

Choose pipeline type with the decision tree below; then follow the Core Workflow.

Quick start: 1) Use the decision tree. 2) Follow the Core Workflow. 3) Use patterns and references as needed.

Pipeline Type Decision Tree

When a user requests a dlt pipeline, determine which type to create:

START: User wants to create a dlt pipeline
│
├─→ Is there a dlt verified source available for this platform?
│   (Check: https://dlthub.com/docs/dlt-ecosystem/verified-sources)
│   │
│   YES → Use VERIFIED SOURCE approach
│   │     Examples: Salesforce, GitHub, Stripe, HubSpot, Slack
│   │     Action: Guide user through `dlt init <source> <destination>`
│   │
│   NO → Continue to next question
│
├─→ Is this a REST API with standard patterns?
│   (Standard auth, pagination, JSON responses)
│   │
│   YES → Use DECLARATIVE REST API approach
│   │     Examples: Pokemon API, simple REST APIs with clear endpoints
│   │     Action: Create config-based pipeline with rest_api_source
│   │
│   NO → Continue to next question
│
└─→ Does this require custom logic or Python packages?
    │
    YES → Use CUSTOM PYTHON approach
          Examples: Python packages (simple-salesforce), complex transformations,
                   non-standard APIs, custom data sources
          Action: Create custom source with @dlt.source and @dlt.resource decorators

Core Workflow

1. Understand Requirements

Ask clarifying questions:

• Source: What is the data source? (API URL, platform name, database, etc.)
• Source type: Does this match a verified source, REST API, or require custom code?
• Destination: Where should data be loaded? (DuckDB, BigQuery, Snowflake, etc.)
• Resources: What specific data/endpoints are needed?
• Incremental: Should the pipeline load incrementally or do full refreshes?
• Authentication: What credentials are required?

2. Choose Pipeline Approach

Based on the decision tree above, select:

• Verified source – Pre-built, tested connector
• Declarative REST API – Config-based REST API pipeline
• Custom Python – Full control with Python code

3. Initialize or Create Pipeline

Verified source

dlt init <source_name> <destination_name>

Examples:

• dlt init salesforce bigquery
• dlt init github duckdb
• dlt init stripe snowflake

Declarative REST API or Custom Python

Use templates from this skill’s assets/templates/ (copy into the project if needed):

• declarative_rest_pipeline.py – For REST APIs
• custom_python_pipeline.py – For custom sources

4. Install Required Packages

Recommended: Use the helper script (detects pip/uv/poetry):

python scripts/install_packages.py --destination <destination_name>

Manual: pip install "dlt[<destination>,workspace]" (e.g. bigquery, snowflake). For DuckDB use dlt[workspace] only. The workspace extra is required for dlt pipeline <name> show and the dashboard.

5. Configure Credentials

Create or update .dlt/secrets.toml:

Structure:

[sources.<source_name>]
# Source credentials here

[destination.<destination_name>]
# Destination credentials here

Use the template: assets/templates/.dlt/secrets.toml

Important: Remind user to add .dlt/secrets.toml to .gitignore!

Note for DuckDB: DuckDB doesn’t require credentials in secrets.toml. Just specify the database file path in the pipeline or config.toml.

6. Configure Pipeline Settings

Create or update .dlt/config.toml for non-sensitive settings:

[sources.<source_name>]
base_url = "https://api.example.com"
timeout = 30

[destination.<destination_name>]
location = "US"

Use the template: assets/templates/.dlt/config.toml

7. Implement Pipeline Logic

Flesh out the pipeline code based on requirements:

For verified sources:

• Customize resource selection with .with_resources()
• Configure incremental loading with .apply_hints()
• See: references/verified-sources.md

For Declarative REST API:

• Define client configuration (base_url, auth)
• Configure resources and endpoints
• Set up pagination and incremental loading
• Resource-level options (e.g. max_table_nesting, table_name) are set in the resource dict in the config; see references/rest-api-source.md Resource configuration.
• See: references/rest-api-source.md

For Custom Python:

• Implement @dlt.source and @dlt.resource functions
• Use generators and yield patterns
• Configure write dispositions and primary keys
• See: references/custom-sources.md

8. Configure Incremental Loading (If Needed)

For pipelines that should load only new/changed data:

• Identify cursor field (timestamp, ID)
• Set write disposition to merge
• Define primary key for deduplication
• Configure incremental parameters

See: references/incremental-loading.md

9. Test and Run Pipeline

python <pipeline_file>.py

Check for errors and verify data is loaded correctly.

10. Inspect Results

Prerequisite: Ensure dlt[workspace] is installed (included by default when using install_packages.py).

Open the dlt dashboard to inspect loaded data:

dlt pipeline <pipeline_name> show

Or use the helper script:

python scripts/open_dashboard.py <pipeline_name>

Pipeline Patterns

Pattern 1: Verified source — Select specific resources

from salesforce import salesforce_source

source = salesforce_source()
pipeline = dlt.pipeline(
    pipeline_name='salesforce_pipeline',
    destination='bigquery',
    dataset_name='salesforce_data'
)

# Load only specific Salesforce objects
pipeline.run(source.with_resources("Account", "Opportunity", "Contact"))

Pattern 2: Declarative REST API – Simple Endpoints

from dlt.sources.rest_api import rest_api_source

config = {
    "client": {
        "base_url": "https://pokeapi.co/api/v2/",
    },
    "resources": [
        "pokemon",
        {
            "name": "pokemon_details",
            "endpoint": "pokemon/{name}",
            "write_disposition": "merge",
            "primary_key": "id"
        }
    ]
}

pipeline = dlt.pipeline(
    pipeline_name="pokemon",
    destination="duckdb",
    dataset_name="pokemon_data"
)
pipeline.run(rest_api_source(config))

Pattern 3: Custom Python – Using Python Package

import dlt
from simple_salesforce import Salesforce

@dlt.source
def salesforce_custom(username=dlt.secrets.value, password=dlt.secrets.value):
    sf = Salesforce(username=username, password=password)

    @dlt.resource(write_disposition='merge', primary_key='Id')
    def accounts():
        records = sf.query_all("SELECT Id, Name FROM Account")
        yield records['records']

    return accounts

pipeline = dlt.pipeline(
    pipeline_name='salesforce_custom',
    destination='duckdb',
    dataset_name='salesforce'
)
pipeline.run(salesforce_custom())

Pattern 4: Incremental Loading with REST API

config = {
    "client": {
        "base_url": "https://api.github.com/repos/dlt-hub/dlt/",
        "auth": {"token": dlt.secrets["github_token"]}
    },
    "resources": [
        {
            "name": "issues",
            "endpoint": {
                "path": "issues",
                "params": {
                    "state": "all",
                    "since": "{incremental.start_value}"
                }
            },
            "incremental": {
                "cursor_path": "updated_at",
                "initial_value": "2024-01-01T00:00:00Z"
            },
            "write_disposition": "merge",
            "primary_key": "id"
        }
    ]
}

Pattern 5: Non-endpoint resources for REST API sources (e.g. Database-Seeded or File-Seeded parameters)

Use non-endpoint resources (e.g. Database-Seeded or File-Seeded parameters) to drive REST API calls from a database, file, or other non-API source. Pre-fetch data outside the dlt pipeline context to avoid dlt.attach() / context conflicts. The seed resource must yield a list of dicts so each row drives one API request.

import duckdb
import dlt
from dlt.sources.rest_api import rest_api_source

# 1. Pre-fetch data from database (outside dlt context)
def get_locations():
    conn = duckdb.connect("locations.duckdb", read_only=True)
    result = conn.execute("SELECT id, lat, lng FROM locations").fetchall()
    conn.close()
    return [{"id": r[0], "lat": r[1], "lng": r[2]} for r in result]

# 2. Create seed resource
@dlt.resource(selected=False)
def locations():
    yield get_locations()  # Yield as LIST

# 3. Configure REST API with resolve
config = {
    "client": {"base_url": "https://api.weather.com/"},
    "resources": [
        locations(),
        {
            "name": "weather",
            "endpoint": {
                "path": "forecast",
                "params": {
                    "lat": "{resources.locations.lat}",
                    "lng": "{resources.locations.lng}"
                },
                "data_selector": "$",
                "paginator": "single_page"
            },
            "include_from_parent": ["id"],
            "primary_key": "_locations_id"
        }
    ]
}

source = rest_api_source(config)
pipeline = dlt.pipeline(
    pipeline_name="weather",
    destination="duckdb",
    dataset_name="weather_data"
)
pipeline.run(source)

See: references/rest-api-source.md (Non-REST Endpoint Resources, Query/Path Params, Single-Object Responses, include_from_parent).

Best Practices (Data Engineering)

• Secrets: Use .dlt/secrets.toml; never hardcode; add to .gitignore
• Primary keys: Set for merge operations and deduplication
• Write dispositions: append (events), merge (stateful), replace (snapshots)
• Performance: Yield pages not rows; use incremental loading when possible

See references/performance-tuning.md, references/incremental-loading.md, and references/troubleshooting.md for more.

Common Challenges and Solutions

Auth (OAuth2): In REST config use "auth": {"type": "oauth2_client_credentials", ...}. For custom Python use dlt.sources.helpers.rest_client.auth.OAuth2ClientCredentials with paginate(). See references/rest-api-source.md.

Custom pagination / nested data / performance: See references/rest-api-source.md, references/custom-sources.md, references/performance-tuning.md.

Reference Documentation — When to Read What

• Full workflow / step-by-step example → references/examples.md
• Verified source → references/verified-sources.md
• Declarative REST API → references/rest-api-source.md
• Custom Python source → references/custom-sources.md
• Incremental loading → references/incremental-loading.md
• Performance → references/performance-tuning.md
• Errors / debugging → references/troubleshooting.md
• dlt basics → references/core-concepts.md

Templates and Scripts

Templates (assets/templates/)

• custom_python_pipeline.py – Custom Python pipeline skeleton
• verified_source_pipeline.py – Verified source pipeline skeleton
• declarative_rest_pipeline.py – Declarative REST API pipeline skeleton
• .dlt/config.toml – Configuration file template
• .dlt/secrets.toml – Secrets file template
• .gitignore – Git ignore template for dlt projects

Scripts (scripts/)

• install_packages.py – Install dlt + destination extras (includes workspace). Run when setting up a new project or adding a destination.
• open_dashboard.py – Open pipeline dashboard (dlt pipeline <name> show). Run after a pipeline run to inspect loaded data.

Key Reminders

• Always ask about destination – Don’t assume
• Security first – Never commit secrets; use .dlt/secrets.toml and provide .gitignore
• Start simple – Use verified sources when available; test incrementally
• Read references – Load detailed docs only when needed