diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index a3aefbce..69d6b902 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -147,6 +147,11 @@ updates:
     schedule:
       interval: "daily"
 
+  - directory: "/framework/dlt"
+    package-ecosystem: "pip"
+    schedule:
+      interval: "daily"
+
   - directory: "/framework/flink/kafka-jdbcsink-java"
     package-ecosystem: "docker-compose"
     schedule:
diff --git a/.github/workflows/framework-dlt.yml b/.github/workflows/framework-dlt.yml
new file mode 100644
index 00000000..32374275
--- /dev/null
+++ b/.github/workflows/framework-dlt.yml
@@ -0,0 +1,73 @@
+name: dlt
+
+on:
+  pull_request:
+    paths:
+    - '.github/workflows/framework-dlt.yml'
+    - 'framework/dlt/**'
+    - '/requirements.txt'
+  push:
+    branches: [ main ]
+    paths:
+    - '.github/workflows/framework-dlt.yml'
+    - 'framework/dlt/**'
+    - '/requirements.txt'
+
+  # Allow job to be triggered manually.
+  workflow_dispatch:
+
+  # Run job each night after CrateDB nightly has been published.
+  schedule:
+    - cron: '0 3 * * *'
+
+# Cancel in-progress jobs when pushing to the same branch.
+concurrency:
+  cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.ref_name }}
+
+jobs:
+  test:
+    name: "
+     Python: ${{ matrix.python-version }}
+     CrateDB: ${{ matrix.cratedb-version }}
+     on ${{ matrix.os }}"
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ 'ubuntu-latest' ]
+        python-version: [ '3.9', '3.13' ]
+        cratedb-version: [ 'nightly' ]
+
+    services:
+      cratedb:
+        image: crate/crate:${{ matrix.cratedb-version }}
+        ports:
+          - 4200:4200
+          - 5432:5432
+        env:
+          CRATE_HEAP_SIZE: 4g
+
+    steps:
+
+      - name: Acquire sources
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          architecture: x64
+          cache: 'pip'
+          cache-dependency-path: |
+            requirements.txt
+            framework/dlt/requirements.txt
+            framework/dlt/requirements-dev.txt
+
+      - name: Install utilities
+        run: |
+          pip install -r requirements.txt
+
+      - name: Validate framework/dlt
+        run: |
+          ngr test --accept-no-venv framework/dlt
diff --git a/framework/dlt/.dlt/config.toml b/framework/dlt/.dlt/config.toml
new file mode 100644
index 00000000..466650e5
--- /dev/null
+++ b/framework/dlt/.dlt/config.toml
@@ -0,0 +1,12 @@
+# Put your main configuration values here.
+#add_dlt_id = false
+#add_dlt_load_id = false
+
+[runtime]
+
+# The system log level of dlt.
+log_level="DEBUG"
+
+# Use the `dlthub_telemetry` setting to enable/disable anonymous
+# usage data reporting, see https://dlthub.com/docs/reference/telemetry.
+dlthub_telemetry = false
diff --git a/framework/dlt/.dlt/secrets.toml b/framework/dlt/.dlt/secrets.toml
new file mode 100644
index 00000000..40026ec0
--- /dev/null
+++ b/framework/dlt/.dlt/secrets.toml
@@ -0,0 +1,26 @@
+[destination.cratedb.credentials]
+# CrateDB PostgreSQL interface
+host = "localhost"
+port = 5432
+username = "crate"
+password = ""
+
+[destination.sqlalchemy.credentials]
+# CrateDB HTTP interface
+# https://dlthub.com/docs/dlt-ecosystem/destinations/sqlalchemy
+drivername = "crate"
+host = "localhost"
+port = 4200
+database = ""
+username = "crate"
+password = ""
+
+[sources.sql_database.credentials]
+# CrateDB HTTP interface
+# https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database/setup
+drivername = "crate"
+host = "localhost"
+port = 4200
+database = ""
+username = "crate"
+password = ""
diff --git a/framework/dlt/.gitignore b/framework/dlt/.gitignore
new file mode 100644
index 00000000..1196403b
--- /dev/null
+++ b/framework/dlt/.gitignore
@@ -0,0 +1,11 @@
+# ignore secrets, virtual environments and typical python compilation artifacts
+# remark: Add it in this case, in order to provide out-of-the-box settings for localhost
+# secrets.toml
+# ignore basic python artifacts
+.env
+**/__pycache__/
+**/*.py[cod]
+**/*$py.class
+# ignore duckdb
+*.duckdb
+*.wal
diff --git a/framework/dlt/README.md b/framework/dlt/README.md
new file mode 100644
index 00000000..bc072ebb
--- /dev/null
+++ b/framework/dlt/README.md
@@ -0,0 +1,70 @@
+# dlt with CrateDB example
+
+## About
+Demonstrate connectivity from dlt to CrateDB.
+
+## Configuration
+Configure database connection address and credentials in `.dlt/secrets.toml`.
+Please make sure to use valid credentials matching your environment.
+
+For [CrateDB] on localhost, a default configuration snippet looks like this.
+```toml
+[destination.cratedb.credentials]
+host = "localhost"                       # CrateDB server host.
+port = 5432                              # CrateDB PostgreSQL TCP protocol port, default is 5432.
+username = "crate"                       # CrateDB username, default is usually "crate".
+password = ""                            # CrateDB password, if any.
+```
+
+For [CrateDB Cloud], a configuration snippet looks like this.
+```toml
+[destination.cratedb.credentials]
+host = "<CLUSTERNAME>.eks1.eu-west-1.aws.cratedb.net"    # CrateDB server host.
+port = 5432                                              # CrateDB PostgreSQL TCP protocol port, default is 5432.
+username = "admin"                                       # CrateDB username, default is usually "admin".
+password = "<PASSWORD>"                                  # CrateDB password, if any.
+```
+
+## Usage
+
+Install dependencies.
+```shell
+pip install -r requirements.txt
+```
+
+Invoke two example pipelines.
+```shell
+python basic.py
+python pokemon.py
+```
+
+## Appendix
+
+### CrateDB on localhost
+Start a CrateDB instance on your machine.
+```shell
+docker run -it --rm \
+  --publish=4200:4200 --publish=5432:5432 \
+  --env=CRATE_HEAP_SIZE=2g \
+  crate:latest -Cdiscovery.type=single-node
+```
+
+### Sandbox
+Acquire `cratedb-example` repository, and set up a development sandbox.
+```shell
+git clone https://github.com/crate/cratedb-examples
+cd cratedb-examples
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+### Software tests
+Invoke the integration test cases.
+```shell
+ngr test framework/dlt
+```
+
+
+[CrateDB]: https://github.com/crate/crate
+[CrateDB Cloud]: https://console.cratedb.cloud/
diff --git a/framework/dlt/basic.py b/framework/dlt/basic.py
new file mode 100644
index 00000000..5beb741f
--- /dev/null
+++ b/framework/dlt/basic.py
@@ -0,0 +1,148 @@
+"""The Intro Pipeline Template contains the example from the docs intro page"""
+import os
+from typing import Optional
+import pandas as pd
+import sqlalchemy as sa
+
+import dlt
+from dlt.sources.helpers import requests
+
+
+CRATEDB_ADDRESS = os.getenv("CRATEDB_ADDRESS", "postgresql://crate:@localhost:5432/")
+
+
+def load_api_data() -> None:
+    """Load data from the chess api, for more complex examples use our rest_api source"""
+
+    # Create a dlt pipeline that will load
+    # chess player data to the CrateDB destination
+    pipeline = dlt.pipeline(
+        pipeline_name="from_api",
+        destination=dlt.destinations.cratedb(CRATEDB_ADDRESS),
+        dataset_name="doc",
+    )
+
+    # Grab some player data from Chess.com API
+    data = []
+    for player in ["magnuscarlsen", "rpragchess"]:
+        response = requests.get(f"https://api.chess.com/pub/player/{player}", timeout=30)
+        response.raise_for_status()
+        data.append(response.json())
+
+    # Extract, normalize, and load the data
+    load_info = pipeline.run(
+        data=data,
+        table_name="chess_players",
+    )
+    print(load_info)  # noqa: T201
+
+
+def load_pandas_data() -> None:
+    """Load data from a public csv via pandas"""
+
+    owid_disasters_csv = (
+        "https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/"
+        "Natural%20disasters%20from%201900%20to%202019%20-%20EMDAT%20(2020)/"
+        "Natural%20disasters%20from%201900%20to%202019%20-%20EMDAT%20(2020).csv"
+    )
+    df = pd.read_csv(owid_disasters_csv)
+
+    pipeline = dlt.pipeline(
+        pipeline_name="from_csv",
+        destination=dlt.destinations.cratedb(CRATEDB_ADDRESS),
+        dataset_name="doc",
+    )
+    load_info = pipeline.run(
+        data=df,
+        table_name="natural_disasters",
+    )
+
+    print(load_info)  # noqa: T201
+
+
+def load_sql_data() -> None:
+    """Load data from a sql database with sqlalchemy, for more complex examples use our sql_database source"""
+
+    # Use any SQL database supported by SQLAlchemy, below we use a public
+    # MySQL instance to get data.
+    # NOTE: you'll need to install pymysql with `pip install pymysql`
+    # NOTE: loading data from public mysql instance may take several seconds
+    # NOTE: this relies on external public database availability
+    engine = sa.create_engine(
+        "mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam"
+    )
+
+    with engine.connect() as conn:
+        # Select genome table, stream data in batches of 100 elements
+        query = "SELECT * FROM genome LIMIT 1000"
+        rows = conn.execution_options(yield_per=100).exec_driver_sql(query)
+
+        pipeline = dlt.pipeline(
+            pipeline_name="from_database",
+            destination=dlt.destinations.cratedb(CRATEDB_ADDRESS),
+            dataset_name="doc",
+        )
+
+        # Convert the rows into dictionaries on the fly with a map function
+        load_info = pipeline.run(
+            data=(dict(row._mapping) for row in rows),
+            table_name="genome",
+        )
+
+    print(load_info)  # noqa: T201
+
+
+@dlt.resource(write_disposition="replace")
+def github_api_resource(api_secret_key: Optional[str] = dlt.secrets.value):
+    from dlt.sources.helpers.rest_client import paginate
+    from dlt.sources.helpers.rest_client.auth import BearerTokenAuth
+    from dlt.sources.helpers.rest_client.paginators import HeaderLinkPaginator
+
+    url = "https://api.github.com/repos/dlt-hub/dlt/issues"
+
+    # Github allows both authenticated and non-authenticated requests (with low rate limits)
+    auth = BearerTokenAuth(api_secret_key) if api_secret_key else None
+    for page in paginate(
+        url,
+        auth=auth,
+        paginator=HeaderLinkPaginator(),
+        params={"state": "open", "per_page": "100"},
+    ):
+        yield page
+
+
+@dlt.source
+def github_api_source(api_secret_key: Optional[str] = dlt.secrets.value):
+    return github_api_resource(api_secret_key=api_secret_key)
+
+
+def load_github_data() -> None:
+    """Load GitHub issues data using the github_api_source."""
+    pipeline = dlt.pipeline(
+        pipeline_name="github_api_pipeline",
+        destination=dlt.destinations.cratedb(CRATEDB_ADDRESS),
+        dataset_name="doc",
+    )
+    load_info = pipeline.run(
+        data=github_api_source(),
+        table_name="github_api_data",
+    )
+    print(load_info)  # noqa: T201
+
+
+def main():
+    functions = [
+        load_api_data,
+        load_pandas_data,
+        load_sql_data,
+        load_github_data,
+    ]
+    for func in functions:
+        try:
+            func()
+        except Exception as e:
+            print(f"Error in {func.__name__}: {e}")  # noqa: T201
+
+
+if __name__ == "__main__":
+    main()
diff --git a/framework/dlt/pokemon.py b/framework/dlt/pokemon.py
new file mode 100644
index 00000000..c8375c50
--- /dev/null
+++ b/framework/dlt/pokemon.py
@@ -0,0 +1,49 @@
+"""data load tool (dlt) — the open-source Python library for data loading
+
+How to create a data loading pipeline with dlt and CrateDB in 3 seconds:
+
+0. Configure `cratedb` destination in `.dlt/secrets.toml`.
+  ```toml
+  [destination.cratedb.credentials]
+  host = "localhost"
+  port = 5432
+  username = "crate"
+  password = ""
+  ```
+
+1. Write a pipeline script
+>>> import dlt
+>>> from dlt.sources.helpers import requests
+>>> dlt.run(
+...     data=requests.get("https://pokeapi.co/api/v2/pokemon/").json()["results"],
+...     destination="cratedb",
+...     dataset_name="doc",
+...     table_name="pokemon")
+
+2. Run your pipeline script
+  > $ python pokemon.py
+
+3. See and query your data with autogenerated Streamlit app
+  > $ dlt pipeline dlt_pokemon show
+
+Or start with our pipeline template with sample PokeAPI (pokeapi.co) data loaded to bigquery
+
+  > $ dlt init pokemon bigquery
+
+For more detailed info, see https://dlthub.com/docs/intro
+"""
+
+
+def main():
+    import dlt
+    from dlt.sources.helpers import requests
+
+    dlt.run(
+        data=requests.get("https://pokeapi.co/api/v2/pokemon/").json()["results"],
+        destination="cratedb",
+        dataset_name="doc",
+        table_name="pokemon")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/framework/dlt/pyproject.toml b/framework/dlt/pyproject.toml
new file mode 100644
index 00000000..8cd2294a
--- /dev/null
+++ b/framework/dlt/pyproject.toml
@@ -0,0 +1,12 @@
+[tool.pytest.ini_options]
+minversion = "2.0"
+addopts = """
+  -rfEX -p pytester --strict-markers --verbosity=3
+  --capture=no
+  """
+log_level = "DEBUG"
+log_cli_level = "DEBUG"
+testpaths = ["*.py"]
+xfail_strict = true
+markers = [
+]
diff --git a/framework/dlt/requirements-dev.txt b/framework/dlt/requirements-dev.txt
new file mode 100644
index 00000000..f24557a6
--- /dev/null
+++ b/framework/dlt/requirements-dev.txt
@@ -0,0 +1,3 @@
+cratedb-toolkit>=0.0.33
+pueblo[sfa]
+pytest<9
diff --git a/framework/dlt/requirements.txt b/framework/dlt/requirements.txt
new file mode 100644
index 00000000..f4ef8fb9
--- /dev/null
+++ b/framework/dlt/requirements.txt
@@ -0,0 +1,9 @@
+# Development
+dlt[cratedb,parquet] @ git+https://github.com/crate-workbench/dlt@cratedb
+
+# Production
+# TODO: switch to official dlt[cratedb]>=1.12.0 once it’s published with CrateDB support.
+# dlt[cratedb,parquet]>=1.12.0
+
+pandas>=2,<2.4
+pymysql>=1,<1.2
diff --git a/framework/dlt/test.py b/framework/dlt/test.py
new file mode 100644
index 00000000..d72e5657
--- /dev/null
+++ b/framework/dlt/test.py
@@ -0,0 +1,89 @@
+import logging
+
+import pytest
+from cratedb_toolkit.util.database import DatabaseAdapter
+from pueblo.sfa.core import run
+
+logger = logging.getLogger()
+
+
+@pytest.fixture(scope="session")
+def db():
+    return DatabaseAdapter("crate://")
+
+
+def test_basic_load_api(db):
+    """
+    Verify dlt pipeline loading data from an API.
+    """
+
+    # Invoke database import.
+    run("basic.py:load_api_data", {})
+
+    # Validate database content.
+    db.refresh_table("doc.chess_players")
+    records = db.run_sql("SELECT * FROM doc.chess_players", records=True)
+    assert len(records) >= 2
+    names = [record["name"] for record in records]
+    assert "Magnus Carlsen" in names
+    assert "Praggnanandhaa Rameshbabu" in names
+
+
+def test_basic_load_pandas(db):
+    """
+    Verify dlt pipeline loading data from a pandas dataframe that has been populated from CSV data.
+    """
+
+    # Invoke database import.
+    run("basic.py:load_pandas_data", {})
+
+    # Validate database content.
+    db.refresh_table("doc.natural_disasters")
+    records = db.run_sql("SELECT * FROM doc.natural_disasters", records=True)
+    assert len(records) >= 837
+    entities = [record["entity"] for record in records]
+    assert "Flood" in entities
+    assert "Landslide" in entities
+    assert "Wildfire" in entities
+
+
+def test_basic_load_sql(db):
+    """
+    Verify dlt pipeline loading data from an SQL database.
+    """
+
+    # Invoke database import.
+    run("basic.py:load_sql_data", {})
+
+    # Validate database content.
+    db.refresh_table("doc.genome")
+    records = db.run_sql("SELECT * FROM doc.genome", records=True)
+    assert len(records) >= 1000
+
+
+def test_basic_load_github(db):
+    """
+    Verify dlt pipeline loading data from GitHub.
+    """
+
+    # Invoke database import.
+    run("basic.py:load_github_data", {})
+
+    # Validate database content.
+    db.refresh_table("doc.github_api_data")
+    records = db.run_sql("SELECT * FROM doc.github_api_data", records=True)
+    assert len(records) >= 277
+
+
+def test_pokemon(db):
+    """
+    Verify the dlt pokemon example.
+    """
+
+    # Invoke database import.
+    run("pokemon.py:main", {})
+
+    # Validate database content.
+    db.refresh_table("doc.pokemon")
+    records = db.run_sql("SELECT * FROM doc.pokemon", records=True)
+    assert len(records) >= 20